What is AgentTel's primary purpose?

AgentTel enriches OpenTelemetry telemetry with structured, AI-actionable context. This empowers AI agents to autonomously diagnose, reason about, and resolve production incidents without human interpretation of dashboards.

How does AgentTel enhance OpenTelemetry for AI agents?

AgentTel enhances standard OpenTelemetry by adding critical context across three levels: Topology (service identity, dependencies), Baselines (what 'normal' looks like for operations), and Decisions (what an AI agent is allowed to do, like retry or escalate).

Which programming languages and environments does AgentTel support?

AgentTel offers multi-language support for diverse tech stacks, including Java, Go, Python, Node.js/TypeScript backends, and browser frontend environments, ensuring comprehensive full-stack observability.

Does AgentTel provide observability for GenAI applications and AI agents?

Yes, AgentTel includes specialized instrumentation for popular GenAI frameworks like Spring AI and LangChain4j. It also offers full lifecycle tracing for AI agents, covering reasoning steps, tool calls, and cost aggregation.

AgentTel

Name: AgentTel
Author: AgentTel

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Enriches OpenTelemetry telemetry with structured context, enabling AI agents to autonomously diagnose, reason about, and resolve production incidents across diverse tech stacks.

AgentTel

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Enriches OpenTelemetry telemetry with structured context, enabling AI agents to autonomously diagnose, reason about, and resolve production incidents across diverse tech stacks.

AgentTel enhances OpenTelemetry telemetry by embedding the structured context necessary for AI agents to autonomously diagnose, reason about, and resolve production incidents, eliminating the need for human interpretation of dashboards. It supports a full-stack approach, covering JVM backends (Java, Kotlin, Scala), Go backends, Node.js/TypeScript backends, Python backends (FastAPI, Django, Flask), and browser frontends (TypeScript/JavaScript), shifting observability from merely answering 'What happened?' to providing 'What does an AI agent need to know to act on this?'

주요 기능

01Enriches OpenTelemetry spans with AI-actionable context for topology, baselines, and decisions.

02Provides a Model Context Protocol (MCP) Server as an AI agent interface for system interaction.

03Offers multi-language support including Java, Go, Python, Node.js, and browser environments.

04Includes specialized observability for GenAI applications and full lifecycle tracing for AI agents.

05Automates instrumentation setup and configuration via an IDE-integrated agent.

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사용 사례

01Enable AI agents for autonomous incident response in production environments.

02Enhance real-time diagnosis and resolution of performance and error anomalies.

03Improve observability and traceability for LLM-powered applications and AI agent workflows.