AI agents, especially those powered by large language models (LLMs), depend on observability and explainability to make their actions trackable and their decisions understandable. Open frameworks provide the tools to achieve this, ensuring agents remain reliable and transparent.
Observable: Real-Time Monitoring
Agents can be watched as they operate, logging actions and states for visibility into their behavior, like tracking task execution or resource use.
Examples:
- OpenTelemetry: Offers standardized telemetry collection—metrics, logs, traces—with AI-specific conventions, integrating with LangGraph and CrewAI for broad compatibility.
- Langfuse: Captures detailed execution traces for multi-agent systems, like Llama Agents, with visualizations for monitoring planning and interactions.
Explainable: Decision Transparency
Agents reveal the reasoning behind their choices, showing why they acted a certain way, such as justifying a recommendation or flagging an error.
Examples:
- Traceloop (OpenLLMetry): Extracts LLM traces from LangChain or LlamaIndex, exporting them in OpenTelemetry format to explain decisions like response selection.
- Phoenix by Arize: Tracks LLM performance with explainability features—like hallucination detection—linking outputs to inputs for clear reasoning.
Adaptive: Feedback-Driven Improvement
Agents adjust based on observed outcomes, using feedback to refine their behavior over time, such as tweaking responses after user corrections.
Examples:
- TruLens: Evaluates LLM responses post-action with feedback loops, improving quality—like refining a chatbot’s tone.
- Helicone: Logs LLM requests and responses in real time, enabling agents to adapt from usage patterns, like optimizing speed, with a free tier up to 50K logs.
A Clear View of Agents
Through observability, explainability, adaptability, and trustworthiness, agents become transparent partners. Vendors like OpenTelemetry and Langfuse monitor their actions, Traceloop and Phoenix clarify their decisions, TruLens and Helicone refine their skills.