Heron .

Heron

Heron delivers passive agent observability from the network wire — giving you complete visibility into what your LLM agents are actually doing without touching a single line of code. Watch tool calls, multi-step plans, execution loops, and inter-agent relationships unfold in real-time, all reconstructed from raw packet capture with zero SDK integration, no proxy configuration, and no risk to your production traffic.

Product Highlights

• Zero-Instrumentation Deployment: Capture and analyze LLM agent traffic with no SDK, no sidecar, and no proxy in the request path — simply point Heron at live network interfaces or replay .pcap files.
• Agent-Turn Reconstruction: Automatically stitch multi-step interactions (planner → tool → planner → tool) into single, addressable turns rather than fragmented HTTP calls, with native support for Claude Code, OpenAI Codex, Hermes, and OpenClaw.
• Full-Fidelity Visibility: See complete request and response bodies, post-TLS plaintext, with semantic extraction of tool calls, reasoning steps, and execution flows that logs alone cannot reveal.
• Production-Safe Architecture: Built in Rust with a passive, off-path design — the observer can fail without breaking the calls it observes, ensuring zero impact on latency or availability.
• Comprehensive Metrics & Export: Track TTFT, E2E latency, token throughput, cache ratios, and more; export structured training data (SFT trajectories) for fine-tuning in one click.

Use Cases

• Production Debugging: Diagnose why agent runs stall, loop, or retry excessively when logs show only "200 OK" — reconstruct the actual behavior from wire evidence.
• Agent Performance Optimization: Identify latency bottlenecks, inefficient tool usage patterns, and cache miss rates across multi-turn sessions to streamline agent execution.
• Cost & Usage Governance: Monitor token consumption, model routing decisions, and cross-service call patterns to prevent runaway spending and unauthorized model substitutions.
• Training Data Generation: Extract clean, structured trajectory data from production agent sessions for supervised fine-tuning and evaluation dataset creation.
• Shadow Mode Observability: Deploy in regulated or high-availability environments where inline proxies or SDK instrumentation are prohibited or too risky.

Target Audience

Heron is built for platform engineers, ML infrastructure teams, and AI product operators running LLM agents in production who need deep behavioral visibility without the operational burden and risk of traditional instrumentation approaches.