Heron

Wireshark for AI Agents: passive eBPF observability

Developer ToolsArtificial IntelligenceGitHubOpen Source

▲ 0 votes6 commentsLaunched Jun 25, 2026

Daily #8Weekly #75

Heron is a passive network analyzer that reconstructs what your AI agents are actually doing. Zero SDKs. Zero proxy. Hook eBPF to see TLS-encrypted LLM calls and identify which agent process made them.

AI Analysis

📝 Summary

Heron is a passive network analyzer leveraging eBPF for observability of AI agents. It reconstructs actual agent behaviors by hooking into TLS-encrypted LLM calls without any SDKs, proxies, or code modifications, while identifying the responsible agent processes. Core features include Wireshark-like inspection for AI interactions. It addresses key pain points like lack of visibility into opaque AI agent actions, unexpected LLM calls, and debugging difficulties in production. The value proposition is zero-overhead, effortless monitoring to enhance security, reliability, and understanding of AI systems.

📈 Market Timing

The market timing is favorable for 2025-2026 as AI agents and autonomous LLM-based systems see explosive growth, driving demand for advanced observability beyond traditional tracing. With eBPF technology mature in Linux environments, rising concerns over AI security, compliance, and transparency (e.g., emerging regulations), and user needs shifting towards non-intrusive tools, Heron aligns perfectly. Excellent Timing.

✅ Feasibility

Overall feasibility is High. While eBPF kernel programming and TLS handling present technical challenges requiring specialized expertise, the tool builds on mature technologies with no supply chain dependencies. Development and operation costs are moderated as an open-source GitHub project. Compliance risks around network monitoring exist but are manageable. Strong scalability potential in cloud and enterprise Linux setups, though team needs kernel-level skills. High.

🎯 Target Market

Main target users: AI/ML engineers, developers, and DevOps teams building or operating AI agents (demographics: tech professionals aged 25-45). Industries: AI startups, enterprise software, cloud services. Geographic distribution: Global with heavy concentration in US, Europe, and Asia tech hubs. Estimated market size: TAM ~$15B+ AI observability/monitoring market; SAM ~$2B for LLM/AI agent tools; SOM ~$100M+ for specialized passive observability. Core pain points: opaque agent decision-making and external calls. High willingness to pay for enterprise features/support despite open-source core.

⚔️ Competition

Competition level: Medium. Direct competitors: 1. LangSmith (smith.langchain.com), 2. Helicone (helicone.ai), 3. Arize Phoenix (arize.com/phoenix), 4. Langfuse (langfuse.com), 5. OpenLLMetry (github.com/traceloop/openllmetry). Advantages: truly passive with zero code changes, eBPF for encrypted traffic visibility and process attribution unlike SDK-heavy competitors. Disadvantages: limited to Linux/eBPF environments, potentially narrower feature set for full LLM tracing/evaluation compared to established platforms, less brand recognition as a newer open-source tool.

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