--- title: "LangSmith and AgentOps: Elevating AI Agents Observability" description: Discover how LangSmith and AgentOps enhance AI agent observability, unlocking insights to optimize performance and efficiency. image: https://www.elixirclaw.ai/hubfs/langsmith-agentops-ai-agents.png --- How Agentic AI is Transforming the Customer Experience [Discover Now →](https://www.elixirclaw.ai/blog/) [![ElixirClaw](https://www.elixirclaw.ai/hubfs/elixirclaw-logo.svg)](https://www.elixirclaw.ai/) - Platform PLATFORM ## Build, orchestrate and govern AI agents Explore the capabilities behind enterprise agent execution. [Explore Platform ↗](https://www.elixirclaw.ai/platform/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Agentic Orchestration**Coordinate agents across complex enterprise workflows. ↗](https://www.elixirclaw.ai/platform/agentic-orchestration/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/agentic-workflow-icon.svg) **Agentic Automation**Automate tasks and business processes using AI agents. ↗](https://www.elixirclaw.ai/platform/agentic-automation/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-teammates-icon.svg) **Agentic Analytics**Turn enterprise data into actionable intelligence. ↗](https://www.elixirclaw.ai/platform/agentic-analytics/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/integrations-icon.svg) **Responsible AI**Govern AI behavior with transparency and oversight. ↗](https://www.elixirclaw.ai/platform/responsible-ai/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Security and Deployment**Deploy agents with enterprise-grade security controls. ↗](https://www.elixirclaw.ai/platform/security-and-deployment/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/manufacturing-dropdown-icon.svg) **Prompt Management**Manage prompts consistently across agent workflows. ↗](https://www.elixirclaw.ai/platform/prompt-management/) WHY ELIXIRCLAW ### AI agents with enterprise-grade oversight. Explore how ElixirClaw connects intelligent automation with the controls enterprise teams need to operate confidently. [Book a product demo](https://www.elixirclaw.ai/agentic-os/demo/) - Solutions SOLUTIONS ## Solve real business challenges with AI Find use cases for security, operations, finance, compliance and more. [Explore Solutions ↗](https://www.elixirclaw.ai/solutions/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/workshops-icon.svg) **Risk and Compliance Management**Improve risk visibility and regulatory workflows. ↗](https://www.elixirclaw.ai/solutions/risk-and-compliance-management/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Customer Services**Improve experiences with AI-driven service workflows. ↗](https://www.elixirclaw.ai/solutions/customer-service/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/energy-dropdown-icon.svg) **Financial Services**Streamline finance operations and reporting workflows. ↗](https://www.elixirclaw.ai/solutions/financial-services/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/integrations-icon.svg) **Agentic Security Operations**Accelerate threat investigation and response. ↗](https://www.elixirclaw.ai/solutions/agentic-security-operations/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/media-dropdown-icon.svg) **IT Operations and Service Management**Improve incident and service management workflows. ↗](https://www.elixirclaw.ai/solutions/it-operations-and-service-management/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/workshops-icon.svg) **Asset Management**Manage assets across their operational lifecycle. ↗](https://www.elixirclaw.ai/solutions/asset-management/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/gaming-dropdown-icon.svg) **Revenue Cycle Management**Streamline billing, collections and revenue workflows. ↗](https://www.elixirclaw.ai/solutions/revenue-cycle-management/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/media-dropdown-icon.svg) **AI Dashboard for Governance**Monitor AI governance and policy compliance. ↗](https://www.elixirclaw.ai/solutions/ai-governance-dashboard/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/retail-dropdown-icon.svg) **Procurement**Improve sourcing, approvals and supplier workflows. ↗](https://www.elixirclaw.ai/solutions/procurement/) FROM USE CASE TO EXECUTION ### Discover what's possible for your business. Explore how governed AI agents can support real operational challenges across security, service management, finance and compliance without introducing disconnected automation. Map your existing workflows and systems to a practical starting point, with enterprise oversight built into the process from the beginning. [Discuss your use case](https://www.elixirclaw.ai/agentic-os/demo/) - Industries INDUSTRIES ## Enterprise AI across industries Discover agentic workflows aligned with your industry's needs. [All Industries ↗](https://www.elixirclaw.ai/industries/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/finance-dropdown-icon.svg) **Finance**Banking, insurance and financial operations. ↗](https://www.elixirclaw.ai/industries/financial-services/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/manufacturing-dropdown-icon.svg) **Manufacturing**Connected manufacturing and intelligent automation. ↗](https://www.elixirclaw.ai/industries/manufacturing/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/telecom-dropdown-icon.svg) **Telecom**Intelligent operations for connected networks. ↗](https://www.elixirclaw.ai/industries/telecom/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/energy-dropdown-icon.svg) **Energy**Critical infrastructure and utility operations. ↗](https://www.elixirclaw.ai/industries/energy-and-utilities/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/gaming-dropdown-icon.svg) **Gaming**Intelligent systems for gaming experiences. ↗](https://www.elixirclaw.ai/industries/gaming/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/media-dropdown-icon.svg) **Media**Content, distribution and audience operations. ↗](https://www.elixirclaw.ai/industries/media-and-entertainment/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/retail-dropdown-icon.svg) **Retail**Smarter commerce and customer experiences. ↗](https://www.elixirclaw.ai/industries/retail/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Shipping**Maritime logistics and connected fleets. ↗](https://www.elixirclaw.ai/industries/shipping/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/agentic-workflow-icon.svg) **Robotics**Intelligent robotics and industrial automation. ↗](https://www.elixirclaw.ai/industries/robotics/) INDUSTRY-READY AI ### Bring governed AI into your environment. Enterprise AI adoption looks different across financial services, manufacturing, utilities and other industries. Your systems, operational requirements and decision controls shape the right approach. [Explore with our team](https://www.elixirclaw.ai/agentic-os/demo/) - Digital Workers DIGITAL WORKERS ## AI workers for enterprise operations Explore specialist agents for different teams and workflows. [View All Workers ↗](https://www.elixirclaw.ai/digital-workers/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/integrations-icon.svg) **Cohort IQ**Build meaningful engagement with intelligent outreach. ↗](https://www.elixirclaw.ai/digital-workers/cohortiq/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/manufacturing-dropdown-icon.svg) **Agent Evaluation**Evaluate AI performance, reliability and quality. ↗](https://www.elixirclaw.ai/digital-workers/agentevaluation/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-teammates-icon.svg) **Agent Analyst**Turn enterprise data into actionable business insights. ↗](https://www.elixirclaw.ai/digital-workers/agentanalyst/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/workshops-icon.svg) **Agent Force**Automate complex workflows across business systems. ↗](https://www.elixirclaw.ai/digital-workers/agentforce/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/agentic-workflow-icon.svg) **Agent SRE**Improve system reliability with proactive monitoring. ↗](https://www.elixirclaw.ai/digital-workers/agentsre/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Agent HR**Streamline hiring, onboarding and HR operations. ↗](https://www.elixirclaw.ai/digital-workers/agenthr/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/telecom-dropdown-icon.svg) **Agent Search**Find trusted information across enterprise systems. ↗](https://www.elixirclaw.ai/digital-workers/agentsearch/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/energy-dropdown-icon.svg) **Agent Instruct**Deliver intelligent guidance and task assistance. ↗](https://www.elixirclaw.ai/digital-workers/agentinstruct/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/retail-dropdown-icon.svg) **Agent Sketch**Accelerate creative concepts and visual workflows. ↗](https://www.elixirclaw.ai/digital-workers/agentsketch/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/agentic-workflow-icon.svg) **Agent Label**Improve data annotation and labeling workflows. ↗](https://www.elixirclaw.ai/digital-workers/agentlabel/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/integrations-icon.svg) **Autonomousops AI**Enable autonomous execution of IT operations. ↗](https://www.elixirclaw.ai/digital-workers/autonomousops-ai/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/gaming-dropdown-icon.svg) **Agent Pixel**Generate and refine images for creative workflows. ↗](https://www.elixirclaw.ai/digital-workers/agentpixel/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Agent RAI**Support responsible AI with risk and bias checks. ↗](https://www.elixirclaw.ai/digital-workers/agentrai/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-teammates-icon.svg) **Agent QA**Automate quality checks and software testing. ↗](https://www.elixirclaw.ai/digital-workers/agentqa/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/workshops-icon.svg) **Agent IAM**Manage enterprise identities and access controls. ↗](https://www.elixirclaw.ai/digital-workers/agentiam/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/retail-dropdown-icon.svg) **Agent SOC**Strengthen threat monitoring and security response. ↗](https://www.elixirclaw.ai/digital-workers/agentsoc/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/energy-dropdown-icon.svg) **Agent FinOps**Optimize cloud spending and financial operations. ↗](https://www.elixirclaw.ai/digital-workers/agentfinops/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/integrations-icon.svg) **Agent Design**Streamline digital design and creative workflows. ↗](https://www.elixirclaw.ai/digital-workers/agentdesign/) [![](https://www.elixirclaw.ai/hubfs/industry-dropdown/manufacturing-dropdown-icon.svg) **Agent Scrum**Support agile planning, tracking and team delivery. ↗](https://www.elixirclaw.ai/digital-workers/agentscrum/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Agent GRC**Improve governance, risk and compliance workflows. ↗](https://www.elixirclaw.ai/digital-workers/agentgrc/) MEET YOUR DIGITAL WORKFORCE ### Explore agents built for your team. Every team has different operational responsibilities. Discover specialist AI workers for analytics, security, reliability, compliance, service delivery and other enterprise workflows. Explore the available workers, understand their use cases and identify how they could fit into a governed enterprise agent deployment. [See digital workers in action](https://www.elixirclaw.ai/agentic-os/demo/) - Pricing PRICING ## Plan your enterprise AI investment Understand the cost of deploying AI agents across your operations. [Pricing Overview ↗](https://www.elixirclaw.ai/pricing/) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **AI Agent Pricing Calculator**Estimate the effort and cost of agentic AI deployment using workload and implementation scenarios. ↗](https://www.elixirclaw.ai/pricing/agentic-os-cost-calculator/) **Choose the right starting point** Explore the pricing overview to understand the offering, or use the calculator to estimate a scenario before discussing your deployment requirements. PLAN YOUR DEPLOYMENT ### Get clarity before you invest. Understand how the number of agents, implementation scope, enterprise integrations and operational requirements can influence your AI initiative. [Discuss deployment options](https://www.elixirclaw.ai/agentic-os/demo/) - Resources RESOURCES ## Learn, explore and evaluate Find useful perspectives, product capabilities and practical AI resources. [Explore Blog ↗](https://www.elixirclaw.ai/blog) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Blog**Perspectives on agentic AI and enterprise automation. ↗](https://www.elixirclaw.ai/blog) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-teammates-icon.svg) **Agentic Actions**Explore intelligent actions and workflow capabilities. ↗](https://www.elixirclaw.ai/ai-agents) [![](https://www.elixirclaw.ai/hubfs/dropdown-assets/ai-agents-icon.svg) **Insights**Explore enterprise AI concepts and emerging practices. ↗](https://www.elixirclaw.ai/insights) SEE IT IN ACTION ### Turn research into a practical next step. Learn how ElixirClaw connects enterprise systems, intelligent agents and governance controls. Explore the platform with your specific requirements in mind. [Request a product demo](https://www.elixirclaw.ai/agentic-os/demo/) [Get Demo ↗](https://www.elixirclaw.ai/agentic-os/demo/) Explore ElixirClaw Platform ⌄ [Platform Overview](https://www.elixirclaw.ai/platform/) [Agentic Orchestration](https://www.elixirclaw.ai/platform/agentic-orchestration/) [Agentic Automation](https://www.elixirclaw.ai/platform/agentic-automation/) [Agentic Analytics](https://www.elixirclaw.ai/platform/agentic-analytics/) [Responsible AI](https://www.elixirclaw.ai/platform/responsible-ai/) [Security and Deployment](https://www.elixirclaw.ai/platform/security-and-deployment/) [Prompt Management](https://www.elixirclaw.ai/platform/prompt-management/) Solutions ⌄ [Solutions Overview](https://www.elixirclaw.ai/solutions/) [Risk and Compliance Management](https://www.elixirclaw.ai/solutions/risk-and-compliance-management/) [Customer Services](https://www.elixirclaw.ai/solutions/customer-service/) [Digital Supply Chain](https://www.elixirclaw.ai/solutions/digital-suppply-chain/) [Financial Services](https://www.elixirclaw.ai/solutions/financial-services/) [Agentic Security Operations](https://www.elixirclaw.ai/solutions/agentic-security-operations/) [IT Operations and Service Management](https://www.elixirclaw.ai/solutions/it-operations-and-service-management/) [Asset Management](https://www.elixirclaw.ai/solutions/asset-management/) [Revenue Cycle Management](https://www.elixirclaw.ai/solutions/revenue-cycle-management/) [AI Dashboard for Governance](https://www.elixirclaw.ai/solutions/ai-governance-dashboard/) [Procurement](https://www.elixirclaw.ai/solutions/procurement/) Industries ⌄ [Industries Overview](https://www.elixirclaw.ai/industries/) [Finance](https://www.elixirclaw.ai/industries/financial-services/) [Manufacturing](https://www.elixirclaw.ai/industries/manufacturing/) [Telecom](https://www.elixirclaw.ai/industries/telecom/) [Energy](https://www.elixirclaw.ai/industries/energy-and-utilities/) [Gaming](https://www.elixirclaw.ai/industries/gaming/) [Media](https://www.elixirclaw.ai/industries/media-and-entertainment/) [Retail](https://www.elixirclaw.ai/industries/retail/) [Shipping](https://www.elixirclaw.ai/industries/shipping/) [Robotics](https://www.elixirclaw.ai/industries/robotics/) Digital Workers ⌄ [Digital Workers Overview](https://www.elixirclaw.ai/digital-workers/) [Cohort IQ](https://www.elixirclaw.ai/digital-workers/cohortiq/) [Agent Evaluation](https://www.elixirclaw.ai/digital-workers/agentevaluation/) [Agent Analyst](https://www.elixirclaw.ai/digital-workers/agentanalyst/) [Agent Force](https://www.elixirclaw.ai/digital-workers/agentforce/) [Agent SRE](https://www.elixirclaw.ai/digital-workers/agentsre/) [Agent HR](https://www.elixirclaw.ai/digital-workers/agenthr/) [Agent Search](https://www.elixirclaw.ai/digital-workers/agentsearch/) [Agent Instruct](https://www.elixirclaw.ai/digital-workers/agentinstruct/) [Agent Sketch](https://www.elixirclaw.ai/digital-workers/agentsketch/) [Agent Label](https://www.elixirclaw.ai/digital-workers/agentlabel/) [Autonomousops AI](https://www.elixirclaw.ai/digital-workers/autonomousops-ai/) [Agent Pixel](https://www.elixirclaw.ai/digital-workers/agentpixel/) [Agent RAI](https://www.elixirclaw.ai/digital-workers/agentrai/) [Agent QA](https://www.elixirclaw.ai/digital-workers/agentqa/) [Agent IAM](https://www.elixirclaw.ai/digital-workers/agentiam/) [Agent SOC](https://www.elixirclaw.ai/digital-workers/agentsoc/) [Agent FinOps](https://www.elixirclaw.ai/digital-workers/agentfinops/) [Agent Design](https://www.elixirclaw.ai/digital-workers/agentdesign/) [Agent Scrum](https://www.elixirclaw.ai/digital-workers/agentscrum/) [Agent GRC](https://www.elixirclaw.ai/digital-workers/agentgrc/) Pricing ⌄ [Pricing Overview](https://www.elixirclaw.ai/pricing/) [AI Agent Pricing Calculator](https://www.elixirclaw.ai/pricing/agentic-os-cost-calculator/) Resources ⌄ [Blog](https://www.elixirclaw.ai/blog) [Agentic Actions](https://www.elixirclaw.ai/ai-agents) [Insights](https://www.elixirclaw.ai/insights) [Get Demo ↗](https://www.elixirclaw.ai/agentic-os/demo/) [LLMS TXT](https://www.elixirclaw.ai/llms.txt) [LLMS Full TXT](https://www.elixirclaw.ai/llms-full.txt) [AI Context JSON](https://www.elixirclaw.ai/ai-context.json) [AI Agents](https://www.elixirclaw.ai/blog/tag/ai-agents) # LangSmith and AgentOps: Elevating AI Agents Observability [Dr. Jagreet Kaur](https://www.elixirclaw.ai/blog/author/dr-jagreet-kaur) | 17 February 2026 LangSmith and AgentOps: Elevating AI Agents Observability 15:06   ## What Is Agentic Observability and Why Does It Matter for AI Agents? In today’s rapidly advancing AI landscape, agent-based systems—whether built on [Large Language Models (LLMs)](https://www.elixirclaw.ai/blog/transforming-llms-into-large-action-model) or traditional AI frameworks—are increasingly handling complex, autonomous tasks. As these systems take on critical roles in industries like manufacturing, finance, and logistics, the need for robust observability becomes more essential. Observability enables us to gain deep visibility into the inner workings of these agents, helping ensure their transparency, efficiency, and reliability.  This blog explores **LangSmith** and [AgentOps](https://www.xenonstack.com/blog/agentops-ai), two innovative platforms designed to deliver actionable insights into AI systems' operations. These platforms offer tools to monitor, analyze, and optimize the behavior of [AI agents](https://www.elixirclaw.ai/blog/what-are-ai-agents) in real-time, making them indispensable for businesses relying on AI-driven solutions.  ### Key Takeaways 1. **Beyond traditional monitoring**: Agentic Observability traces reasoning paths, tool calls, and decision workflows—not just system health metrics like uptime or latency 2. **LangSmith for LLM pipelines**: Provides end-to-end tracing of prompts, model responses, token usage, latency, and evaluation chains for language model applications 3. **AgentOps for multi-agent systems**: Monitors agent-to-agent communication, collaboration quality, resource allocation, and behavioral deviations in distributed agent architectures 4. **Integration architecture**: Unified observability through API ingestion, custom dashboards, anomaly detection, and real-time alerting—enabling centralized monitoring of heterogeneous agent systems 5. **Critical business outcomes**: Accelerated debugging (hours to minutes), performance optimization (identifying token waste, latency bottlenecks), and trust through transparent decision tracing ## What is Observability in Agentic AI? Observability is essentially gaining insight into how an AI agent or a system of agents works internally, as used in agentic AI. That is far ahead from the traditional monitoring approach that tends to focus on external metrics-the kind of percentage of uptime of a system or network status-to understand how an AI agent or a system of agents functions.  It involves gathering and analyzing data with agent behavior for interaction with environments or other agents, response behavior in answering queries posed to them, efficiency in processing workloads, and error-handling capabilities. Providing observability gives visibility into internal processes clearly giving a better view of how functionalities operate; thus, it supports easier debugging, performance optimization, and identification of potential failure points.  Observability tools, such as **LangSmith** and **AgentOps**, go beyond simple logs and metrics. They offer actionable insights that help developers and operators optimize agent performance.  [Agentic Observability](https://www.xenonstack.com/blog/ai-agent-observability-evaluation-tools) refers to the ability to monitor, trace, analyze, and explain the internal decision-making steps of AI agents, including LLM-based agents and multi-agent systems. Unlike traditional monitoring, Agentic Observability provides visibility into reasoning paths, tool calls, workflows, and interactions across agents. This enables developers to debug, optimize, and trust agentic systems at scale. > ### How is Agentic Observability different from traditional monitoring? > > Traditional monitoring tracks system health; Agentic Observability tracks reasoning, tool usage, and decision paths. ## Why Is Agentic Observability Essential for AI Systems? [Observability](https://www.xenonstack.com/insights/what-is-observability)makes the working of AI agent systems transparent and accountable. Without it, one can hardly understand the "why" behind some decisions, especially in LLM or multi-agent environments. Debugging will be much harder because, even if errors happen, tracing back the roots of the error toward specific decision points will become cumbersome.  Tools like Langsmith, push observability further into the LLMs so traces can be taken for the responses and hence easier to debug those problems on the part of the developer. AgentOps on the other side is for multi-agent systems, allowing teams to track collaboration, interaction, as well as the behavior of the agents.  An [AI Observability Platform](https://www.elixirclaw.ai/assessment/ai-monitoring-observability/) is a unified system that captures logs, traces, metrics, prompts, outputs, tool calls, and decision paths from AI agents and LLM pipelines. Platforms like LangSmith and AgentOps deliver real-time visibility into performance, cost, reliability, and behavioral patterns. AI observability platforms help teams debug LLM outputs, analyze agent behavior, detect anomalies, and maintain trustworthy agentic systems. - **Intelligent Observability**(also called **Cognitive Observability**) uses AI to interpret agent behavior, understand reasoning patterns, detect anomalies, and infer root-cause issues automatically. Instead of manually reviewing logs and traces, cognitive observability systems analyze hallucinations, degraded accuracy, faulty reasoning, and tool-call failures—providing predictive diagnostics that reduce debugging time. - **Agent-based Observability** focuses on monitoring the decisions, actions, transitions, and collaborations of multiple autonomous agents. Tools like AgentOps track agent-to-agent communication, coordination quality, resource usage, and behavioral deviations. This helps developers diagnose bottlenecks, understand multi-agent dynamics, and optimize complex agent workflows. - **Observability with AI Agents** means using AI-driven agents to automate and enhance observability workflows. Observability agents can summarize logs, analyze traces, detect anomalies, classify errors, and generate root-cause explanations automatically. This improves DevOps, SRE, and multi-agent reliability by enabling continuous intelligent oversight. > ### What does an AI Observability Platform provide? > > It provides centralized monitoring of logs, reasoning traces, prompts, and performance metrics. ## How Does It Differ from Traditional Monitoring? ### Comparison: Traditional Monitoring vs Agentic Observability | Dimension | Traditional Monitoring | Agentic Observability | | --- | --- | --- | | **Focus** | System health (uptime, latency, errors) | Reasoning processes (decision paths, tool usage, interactions) | | **Granularity** | Infrastructure-level metrics | Cognitive-level traces (prompts, reasoning steps, actions) | | **Debugging** | Correlate metrics to identify failures | Trace decision chains to understand *why* failures occurred | | **Optimization** | Resource allocation, scaling | Token efficiency, reasoning accuracy, tool selection quality | | **Transparency** | What happened (error occurred) | Why it happened (agent chose incorrect tool due to ambiguous context) | | **Scope** | Single-service monitoring | Multi-agent collaboration and workflow analysis | **Business Impact**: Organizations using agentic observability reduce debugging time by 60-80%, improve model accuracy through prompt optimization, and maintain trust through explainable decision tracing. ## What Are the Core Components of an AI Observability Platform? ### 1. Unified Data Ingestion Layer **Function**: Capture heterogeneous data from distributed agent systems into centralized storage for analysis. **Data Sources**: - **LLM interactions**: Prompts, completions, token counts, latency, model metadata - **Tool invocations**: API calls, function executions, database queries, external service requests - **Agent telemetry**: State transitions, decision points, memory accesses, inter-agent messages - **Performance metrics**: Response times, throughput, error rates, resource utilization **Implementation Pattern**: Agents instrument their operations with OpenTelemetry-compatible tracers, sending structured logs and traces to the observability platform via APIs. ### 2. Trace and Reasoning Visualization **Challenge**: AI agent workflows span multiple steps (planning → tool use → reasoning → action), creating complex execution graphs difficult to interpret from raw logs. **Solution**: Visual trace representations showing: - **Execution timelines**: Chronological view of operations with latency attribution - **Dependency graphs**: Which steps depend on which tool outputs or agent responses - **Reasoning trees**: Hierarchical visualization of goal decomposition and sub-task execution - **Multi-agent choreography**: Message flows and coordination patterns across agents   **LangSmith Example**: Traces display a customer query → document retrieval → prompt construction → LLM reasoning → response generation pipeline, highlighting that 80% of latency occurs in document retrieval (not model inference). ### 3. Performance Analytics and Metrics **Key Metrics for LLM Systems**: - **Token efficiency**: Tokens per task, cost per operation, prompt compression opportunities - **Latency distribution**: P50, P95, P99 response times across reasoning steps - **Accuracy metrics**: Task completion rate, user satisfaction scores, error frequency **Model utilization**: Which models handle which task types, switching logic effectiveness   **Key Metrics for Multi-Agent Systems**: - **Coordination efficiency**: Message overhead, synchronization delays, deadlock frequency - **Load balancing**: Task distribution across agents, idle time, bottleneck identification - **Collaboration quality**: Success rate of delegated tasks, rework frequency - **Resource costs**: Compute per agent, cost attribution to business operations ### 4. Anomaly Detection and Alerting **Anomaly Categories**: **Reasoning Failures**: - Hallucinations (factually incorrect outputs) - Logic errors (invalid inferences despite correct premises) - Tool misuse (calling inappropriate APIs for given context)   **Performance Degradation**: - Latency spikes beyond SLA thresholds - Token consumption exceeding budget limits - Error rate increases **Behavioral Deviations**: - Agents ignoring assigned goals - Unexpected tool usage patterns - Multi-agent deadlocks or infinite loops **Alerting Mechanisms**: Real-time notifications via Slack, PagerDuty, or webhooks when anomalies exceed configured thresholds—enabling rapid incident response. ### 5. Evaluation and Testing Framework **Purpose**: Systematically assess agent performance against benchmarks to validate improvements and detect regressions. **Evaluation Chains (LangSmith)**: - **Accuracy tests**: Compare agent outputs to ground truth on curated datasets - **Consistency checks**: Verify identical inputs produce consistent outputs across runs - **Adversarial testing**: Probe with edge cases, ambiguous inputs, malicious prompts **Multi-Agent Simulations (AgentOps)**: - **Stress testing**: Increase agent count or task load to identify scalability limits - **Fault injection**: Simulate agent failures to test recovery and coordination robustness - **Scenario replay**: Re-run historical traces to validate bug fixes ## How Are LangSmith and AgentOps Implemented in Production Environments? ### **LangSmith Implementation** LangSmith excels particularly in looking at how large language models work: it traces, logs, and analyzes all interactions between users and the model. This allows developers to monitor user queries and model responses and intermediate steps in real-time- and makes it easier to decide what doesn't quite meet output expectations. For example, if the customer service chatbot gives some wrong response, LangSmith enables the developer to go back through the whole conversation and can check- the input of the user or how the model has processed it, or even details like token usage and latency-all of them prove to be rich feeds about the model's efficiency and performance.  #### **How Developers Can Utilize LangSmith:** - **Logging Interactions**: All user-model interaction instances will be logged automatically for analysis and review. - **Performance Metrics**: Model latency, token consumption, and execution time against which the model will be optimized in the continuous improvement process. - **Error Debugging**: Quickly identify incorrect outputs and errors which significantly helps in reducing troubleshooting time and enhancing the debugging process. - **Evaluation Chains**: It employs pre-defined evaluation chains that assess the performance of a model regarding assigned tasks, including precision and relevance. ## **AgentOps Implementation** AgentOps addresses multi-agent systems where many AI agents interact and cooperate to achieve the goals of the systems. A good example might be a fleet of warehouse robots working cooperatively in synchronization to transport packages. AgentOps helps developers monitor the performance of each agent, identify bottlenecks in decision-making processes, and determine if there is one agent that's causing the overall inefficiency of the system.  #### **How Developers Can Utilize AgentOps:** - **Telemetry Data**: It records fairly detailed data about the decisions by agents, transitions, and actions that give comprehensive insight into agent operation. - **Behavioral Monitoring**: Evaluate every decision by each of the agents based on what is supposed to be done by it and the tool executed. - **Real-time Alerts**: In case agents deviate from their assigned tasks or goals, real-time alerts are generated to correct the situation. - **Collaboration Analysis**: It analyses the quality of agent collaborations to make the agent interactions and overall performance better. ### Architecture Diagrams and Explanations ![langsmith-agentops-architecture](https://www.elixirclaw.ai/hs-fs/hubfs/langsmith-agentops-architecture.jpg?width=1920&height=1080&name=langsmith-agentops-architecture.jpg)Fig1: Architecture Diagram of Langsmith and AgentOps - **Core Workflow Engine:** At the core of Akira AI lies its [workflow](https://www.elixirclaw.ai/blog/transforming-operations-with-agentic-ai) engine, which is used to process every kind of data from LangSmith’s LLM observability to AgentOps multi-agent observability and aggregates logs, metrics, traces, and performance data. - **Custom Dashboards**: All observability metrics are displayed here, and can monitor the health and performance of LLM-based applications and multi-agent systems over time. - **Metrics and Traces**: This block stores and processes detailed performance data, including: - **LLM Call Trace**: Provides data on calls to the LLM APIs which, in turn, may allow tracing execution flow, failure detection, and model behaviors. - **Cost Analysis**: It tracks costs related to LLM calls, tracks resource utilization, and measures total operating costs. - **Anomaly Detection & Alerts:** Use the collected data to identify outliers or anomalous behavior related to agent activity or LLM responses to trigger alerts on some potential issues with the system. ### **Running Applications** - **LLM-based Application**: LangSmith tracks interaction, token usage, and other performance metrics of an AI system based on large language models. - **Multi-Agent System**: An AI system in which multiple agents collaborate, monitored by AgentOps to analyze the pattern of interaction, the metrics of collaboration, and resource utilization. ### **LangSmith and AgentOps Observability** - LangSmith constantly checks detailed statistics on LLM conversation traces, token usage, response times, and call traces. All such data is passed systematically through APIs to Akira AI for the central purpose of monitoring. - AgentOps tracks multi-agent interaction logs, collaboration, resource usage, and cost metrics. These insights flow into Akira AI for real-time analysis and optimization of agent workflows.   ## What Are the Key Benefits of Agentic Observability? - **Deep Transparency and Insights:** These observability tools provide complete transparency of the decisions and actions taken by an AI agent. Because each decision is tracked, it provides chances for developers to explore the data and determine mistakes or inefficiencies. - **Accelerated debugging and troubleshooting:** They also unravel the work by providing the developers with filled logs and real-time alerts, thereby enabling them to rapidly identify and debug problems. - **Performance Optimization:** This would help for further optimizations of LLM-based applications. For example, LangSmith lets a developer understand the efficiency with which LLMs process their data, specifically in terms of token usage, response latency, and general efficiency that helps developers pinpoint bottlenecks within the system. - **Scalability and Adaptability:** As the scale of deployments of AI agents is increasing, so should the scalability of observability offered by the firm. AgentOps is designed specifically for monitoring large multi-agent systems, aggregating information from many sources without degradation in performance. ## Where Is Agentic Observability Deployed Across Industries? - **Manufacturing:** Predictive models of maintenance are used to determine the possibility of machine failure in a factory. How the models make decisions can be traced to provide an open insight into how predictions about downtime are formed. This is done to ensure accuracy in the servicing of [equipment and distribution](https://www.elixirclaw.ai/blog/real-time-equipment-monitoring-with-agentic-ai) of resources. - **Customer Service and Chatbots:** For instance, in e-commerce companies, an AI agent may process thousands of queries each day.  If a [customer](https://www.elixirclaw.ai/blog/ai-agents-for-consumer-engagement) gets the wrong or irrelevant reply, then developers can trace exactly where it went wrong whether it is at the model logic level, due to misinterpretation, or poor data inputs. - **Healthcare: Medical Diagnosis Systems**: Accuracy in [healthcare](https://www.elixirclaw.ai/blog/ai-agents-for-healthcare) is crucial. AgentOps can track AI agents used in medical diagnosis and always point out patients' data for diagnosis suggestions. Medical providers can be assured that the suggestions offered by the AI are reliable, not biased, and safe upon observing the decision-making process. - **Finance: Fraud Detection**: In the [finance](https://www.elixirclaw.ai/blog/ai-agent-for-finance)sector, AI agents are employed to identify potentially fraudulent transactions. By monitoring its decision-making processes, organizations can trace the rationale behind each flagged transaction. This transparency allows financial institutions to refine their fraud detection models and reduce false positives, ensuring legitimate transactions proceed smoothly while enhancing security. > ``` > Integration With Akira AI > ``` > > The following steps would allow the integration of LangSmith and AgentOps with Akira AI for effective monitoring and workflow management:  > > - **APIs**: API interfacing from LangSmith and AgentOps can be integrated into the architecture of Akira AI to allow for the smooth-flowing data between tools of observability and those workflows that already exist within Akira. > > - **Custom Dashboards**: Akira AI has built-in the integration of dashboards right into the interface. This allows a seamless view and enables users to view agent performance, watch metrics, and identify problems without changing levels. > > - **Data Ingestion:** Akira AI will ingest telemetry and logs from LangSmith for LLM observability and AgentOps for multi-agent performance monitoring. In other words, all this collection, processing, and analysis falls within the Akira ecosystem. > > - **Alerting System:** It integrates alerting capabilities of LangSmith and AgentOps with Akira AI; in other words, the platform permits a real-time sending of a notification relating to performance anomalies, a bottleneck in decision-making processes, or unexpected agent behavior. > > - **Collaborative Agent Optimization:** Such integrations enable Akira AI to generate collaborative analytics for multi-agent systems, thus optimizing LLM-based workflows where the execution of tasks is efficient and agents interact with each other smoothly. ## What Challenges Exist in Implementing Agentic Observability? Although LangSmith and AgentOps offer the value of observability tools, they do come with their challenges.  - **Performance Overhead**: Agentic systems whose activity constantly goes through logging and monitoring always incur some performance overheads, especially in resource-poor environments. Data collection will include delays in system operation, specifically when involving applications with high traffic or multi-agent systems with many interactions. - **Data Volume Management:** As the complexity of AI agents increases, the volume of [telemetry data](https://cloud.google.com/learn/what-is-opentelemetry?hl=en) is similarly going to escalate. Without proper data management or filtering, the overwhelming amount of information could potentially drown the system and the developers too. Large-scale agent-based systems produce a volume of telemetry data that can't be processed or made meaningful insights from. - **Integration Complexity:** The APIs can allow for integration with LangSmith and AgentOps. Integration processes by their nature are, therefore, technically quite demanding in themselves; and especially where particularly highly specialized or legacy system-to-integrations are concerned- require quite a significant overhaul of existing infrastructure. - **Interpretable Issues:** For most AI applications, observability is robust. It might not be easy, to understand why a particular model or agent behaves in a specific way. More so for [LLMs](https://aws.amazon.com/what-is/large-language-model/), biased or unusual outputs could be only well understood in depth by additional domain expertise. > ### What is the biggest limitation of observability tools? > > Managing large volumes of telemetry data efficiently. ## What Are the Future Trends in Agentic Observability? The observability of AI shortly would be oriented toward self-optimizing agents. They would have the ability to learn from observations and optimize themselves in real-time. This would lead to reducing the need for human intervention for debugging or optimization and hence lead to more autonomous systems.  - **Predictive Observability**: It is another trend gaining momentum. Instead of monitoring agents in real-time on the expected events, the future systems will predict failures or possibly suboptimal behaviors ahead of time, thus allowing for pre-emptive adjustments. - **Advanced Multi-Agent Coordination:** [Observability tools](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/boosting-it-resilience-efforts-through-application-performance-monitoring) will be able to vastly control more complicated multi-agent systems containing tens of cooperating agents. Advanced tracing will have to be improved enough so that developers can trace interactions down to very complex systems in decentralized systems. - **Edge AI Monitoring**: As more AI moves out to the edge, it will prove a nice challenge for observability tools to adapt to such decentralized [edge systems](https://www.elixirclaw.ai/edge-ai-platform/). It brings in a whole new set of challenges and opportunities for ensuring that the level of observability remains as effective in distributed environments as it is on centralized premises. ## Conclusion: Why Agentic Observability with LangSmith and AgentOps Is Critical? As AI agents become more central to critical operations across industries, having robust observability systems like LangSmith and AgentOps will be essential. These platforms not only offer transparency into the inner workings of these systems but also provide powerful tools for debugging, optimizing, and scaling AI operations. From predicting machine failures in factories to managing trading bots in finance, observability transforms the way we manage AI, making systems more reliable, efficient, and understandable.  With the adoption of observability, businesses can realize full capacity in their AI systems, such that agents would be able to act and learn with strategy objectives. The future of observability promises even more automation, predictive power, and adaptability, laying the groundwork for the next generation of intelligent, autonomous systems. ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents%20&body=Check%20out%20https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents) ## Table of Contents ## Explore Related Topics [AI Agents](https://www.elixirclaw.ai/blog/tag/ai-agents) [Finance](https://www.elixirclaw.ai/blog/tag/finance) [Customer Operations](https://www.elixirclaw.ai/blog/tag/customer-operations) [Manufacturing](https://www.elixirclaw.ai/blog/tag/manufacturing) [Banking](https://www.elixirclaw.ai/blog/tag/banking) [Healthcare](https://www.elixirclaw.ai/blog/tag/healthcare) [LLMOps](https://www.elixirclaw.ai/blog/tag/llmops) [Telecom](https://www.elixirclaw.ai/blog/tag/telecom) [Logistics](https://www.elixirclaw.ai/blog/tag/logistics) ![Subscribe background](https://www.xenonstack.com/hubfs/blog-post-subscribe.svg) ## Subscribe to our Latest Technology Insights and Resources Subscribe Now ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Get the latest articles in your inbox Business Email ID \* Please enter a valid Business Email ID Company Name \* Please enter a valid Company Name Yes, I would like to receive the ElixirClaw newsletter as well as marketing emails regarding ElixirClaw products, services, and events. I can unsubscribe at any time. By registering, you confirm that you agree to the processing of your personal data by ElixirClaw as described in the Privacy Policy. Subscribe Now ## Related Articles for you ![From MLOps to GenAIOps: The Evolution of AI Agents Operations](https://www.elixirclaw.ai/hubfs/Mlopstogenaiops.png) ### [From MLOps to GenAIOps: The Evolution of AI Agents Operations](https://www.elixirclaw.ai/blog/from-mlops-to-genaiops) 29 December 2024 ![Scaling Authentic Engagement with CohortIQ Guardrails](https://www.elixirclaw.ai/hubfs/cohortiq-guardrails.png) ### [Scaling Authentic Engagement with CohortIQ Guardrails](https://www.elixirclaw.ai/blog/cohortiq-authentic-engagement) 02 September 2025 ![Unlocking Vision Agents: Best Practices for LMM Deployment](https://www.elixirclaw.ai/hubfs/vision-agents.png) ### [Unlocking Vision Agents: Best Practices for LMM Deployment](https://www.elixirclaw.ai/blog/vision-agents-for-lmm-deployment) 09 December 2025 [![ElixirClaw logo](https://www.elixirclaw.ai/hubfs/elixirclaw-logo.svg)](https://www.elixirclaw.ai/) Enterprise Agentic OS for multi-agent orchestration, workflow automation, and analytics—powering systems of action across business processes and enterprise systems [mailto:info@elixirclaw.ai](mailto:info@elixirclaw.ai) [![databricks-icon](https://www.elixirclaw.ai/hs-fs/hubfs/databricks.png?width=200&height=49&name=databricks.png)](https://www.databricks.com/company/partners) [![nvidia-partner-icon](https://www.elixirclaw.ai/hubfs/nvidia-logo.svg)](https://www.nvidia.com/en-in/startups/) [![aws-partner-icon](https://www.elixirclaw.ai/hubfs/aws_icon.svg)](https://partners.amazonaws.com/) [![aws-ml-elevate-icon](https://www.elixirclaw.ai/hubfs/aws-ml-elevate-icon.svg)](https://aws.amazon.com/machine-learning/partner-solutions/) ## Company [About Us ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/about-us/) [Contact Us ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/contact-us/) [Get Demo ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/agentic-os/demo/) [Blog ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blog) [AI Agents ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/ai-agents/) [Digital Workers ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/digital-workers/) ## Industry [Financial Services ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/financial-services/) [Manufacturing ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/manufacturing/) [Telecom ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/telecom/) [Energy and Utilities ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/energy-and-utilities/) [Gaming ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/gaming/) [Media and Entertainment ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/media-and-entertainment/) [Retail and CPG ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/retail/) [Shipping ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/shipping/) [Robotics ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/industries/robotics/) ## Use Cases [Risk Management ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/risk-and-compliance-management/) [Customer Services ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/customer-service/) [Finance Operations ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/financial-services/) [Agentic Security Operations ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/agentic-security-operations/) [IT Operations ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/it-operations-and-service-management/) [Asset Management ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/asset-management/) [Revenue Cycle Management ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/revenue-cycle-management/) [AI Dashboard for Governance ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/ai-governance-dashboard/) [Procurement ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/solutions/procurement/) ## Blueprints [ElixirClaw for SAP ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-sap/) [ElixirClaw for Oracle Fusion ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-oracle-fusion/) [ElixirClaw for Infor ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-infor/) [ElixirClaw for ServiceNow ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-servicenow/) [ElixirClaw for Workday ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-workday/) [ElixirClaw for Microsoft Dynamics ![arrow-icon](https://www.elixirclaw.ai/hubfs/footer-arrow-icon.svg)](https://www.elixirclaw.ai/blueprints/elixir-claw-for-microsoft-dynamics/) Copyright 2026 - ElixirClaw | Product of ElixirData Terms Privacy Cookie Policy [LLMS TXT](https://www.elixirclaw.ai/llms.txt) [LLMS Full TXT](https://www.elixirclaw.ai/llms-full.txt) [AI Context JSON](https://www.elixirclaw.ai/ai-context.json) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Dr. Jagreet Kaur", "url" : "https://www.elixirclaw.ai/blog/author/dr-jagreet-kaur" }, "dateModified" : "2026-02-17T04:43:47.696Z", "datePublished" : "2024-11-15T09:50:40.000Z", "headline" : "LangSmith and AgentOps: Elevating AI Agents Observability", "image" : [ "https://www.elixirclaw.ai/hubfs/langsmith-agentops-ai-agents.png" ], "mainEntityOfPage" : { "@id" : "https://www.elixirclaw.ai/blog/langsmith-and-agentops-with-ai-agents", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject" }, "name" : "ElixirClaw" } } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/#org", "@type" : "Organization", "description" : "Akira AI is an enterprise Agentic AI platform enabling autonomous AI agents, orchestration, observability, and governed AI execution across enterprise systems.", "email" : "info@akira.ai", "logo" : { "@id" : "https://www.akira.ai/#logo", "@type" : "ImageObject", "url" : "https://www.akira.ai/hubfs/Akira_AI_Logo.png" }, "name" : "Akira AI", "sameAs" : [ "https://www.linkedin.com/company/akira-ai/", "https://twitter.com/akira_ai", "https://www.youtube.com/@akiraai" ], "url" : "https://www.akira.ai/" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#author", "@type" : "Person", "description" : "Dr. Jagreet Kaur specializes in Generative AI, Agentic AI systems, AI observability, governance frameworks, and enterprise AI transformation.", "name" : "Dr. Jagreet Kaur" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#primaryimage", "@type" : "ImageObject", "url" : "https://www.akira.ai/hubfs/langsmith-agentops-ai-agents.png" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#techarticle", "@type" : "TechArticle", "articleSection" : "Agentic AI", "author" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#author" }, "dateModified" : "2026-02-10", "datePublished" : "2026-02-10", "description" : "LangSmith and AgentOps enhance observability, debugging, tracing, and governance for AI agents, enabling reliable and scalable Agentic AI systems in production.", "headline" : "LangSmith and AgentOps with AI Agents", "image" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#primaryimage" }, "keywords" : [ "LangSmith", "AgentOps", "AI Agents", "Agentic AI", "AI Observability", "LLM Monitoring", "AI Governance" ], "mainEntityOfPage" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents", "@type" : "WebPage" }, "publisher" : { "@id" : "https://www.akira.ai/#org" } } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#definedterm-agentops", "@type" : "DefinedTerm", "description" : "AgentOps is the operational discipline focused on monitoring, debugging, evaluating, and governing AI agents running in production environments.", "inDefinedTermSet" : "https://www.akira.ai/blog/tag/agentic-ai", "name" : "AgentOps", "termCode" : "AGENTOPS" } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#faq", "@type" : "FAQPage", "mainEntity" : [ { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#q1", "@type" : "Question", "acceptedAnswer" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#a1", "@type" : "Answer", "text" : "AgentOps refers to the processes and tools used to monitor, debug, evaluate, and govern AI agents operating in real-world production environments." }, "name" : "What is AgentOps in AI systems?" }, { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#q2", "@type" : "Question", "acceptedAnswer" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#a2", "@type" : "Answer", "text" : "LangSmith provides tracing, evaluation, and debugging capabilities that improve transparency, reliability, and performance of AI agent workflows." }, "name" : "How does LangSmith improve AI agent performance?" }, { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#q3", "@type" : "Question", "acceptedAnswer" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#a3", "@type" : "Answer", "text" : "Observability ensures AI agent decisions are traceable, explainable, and aligned with enterprise governance and compliance requirements." }, "name" : "Why is observability important for AI agents?" } ] } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#qapage", "@type" : "QAPage", "mainEntity" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#qa-question", "@type" : "Question", "acceptedAnswer" : { "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#qa-accepted-answer", "@type" : "Answer", "text" : "LangSmith supports AgentOps by enabling detailed tracing, evaluation, and debugging of AI agent executions in production." }, "answerCount" : 1, "name" : "What is the role of LangSmith in AgentOps?" } } ``` ```json { "@context" : "https://schema.org", "@id" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents#breadcrumb", "@type" : "BreadcrumbList", "itemListElement" : [ { "@type" : "ListItem", "item" : "https://www.akira.ai/", "name" : "Home", "position" : 1 }, { "@type" : "ListItem", "item" : "https://www.akira.ai/blog", "name" : "Blog", "position" : 2 }, { "@type" : "ListItem", "item" : "https://www.akira.ai/blog/tag/agentic-ai", "name" : "Agentic AI", "position" : 3 }, { "@type" : "ListItem", "item" : "https://www.akira.ai/blog/langsmith-and-agentops-with-ai-agents", "name" : "LangSmith and AgentOps with AI Agents", "position" : 4 } ] } ```