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K9 Audit

Causal intent-execution audit trail for AI agents

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On March 4, 2026, my Claude Code agent wrote a staging URL into a production config file — three times, 41 minutes apart. Syntax was valid, no error thrown. My logs showed every action. All green.The problem was invisible because nothing had recorded what the agent intended to do before it acted — only what it actually did.K9 Audit fixes this with a causal five-tuple per agent step: - X_t: context (who acted, under what conditions) - U_t: action (what was executed) - Y*_t: intent contract (what it was supposed to do) - Y_t+1: actual outcome - R_t+1: deviation score (deterministic — no LLM, no tokens)Records are SHA256 hash-chained. Tamper-evident. When something goes wrong, `k9log trace --last` gives root cause in under a second.Works with Claude Code (zero-config hook), LangChain, AutoGen, CrewAI, or any Python agent via one decorator.pip install k9audit-hook

HN · launched Mar 12, 2026 · 5 upvotes (top 89%) · 0 views

Niche: ai agent observability platforms · 220 launches, 5 in the last 7 days ▼ -64% · Explore this niche →

Category and tags
  • Horizontal
  • Compliance & governance
  • Developer
  • AI-native
  • Commercial product

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More like this: all 220 launches in ai agent observability platforms →

Could you build this?

Yes It acts as a middleware audit layer or proxy/wrapper around AI agent tool execution that logs declared intent before executing shell or file operations.

Discussion

3 comments analyzed.

Competitors mentioned: K9 (single-agent auditing sealing at execution time)

Concerns raised: Multi-agent delegation gaps remain unsolved, Computing effective policy intersection across delegation chains lacks design, Reconstructing authority state at execution time is the hardest post-incident analysis problem, Using LLM tools to audit LLM agents creates conflict of interest, Non-deterministic systems cannot be properly audited with probability theory alone

Feature requests: First-class DELEGATION records in chain with parent scope and policy version, Effective authority computation at execution time for multi-level delegation, Causal AI observation model for deterministic audit trails, Sealed execution bundles containing input context, identity, permissions, policy version, action, outcome, and cryptographic links

Competitors

Other products that read as similar to this one : 318 launches clear the similarity bar, closest 8 shown.

Attention rank: #301 of 319 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 117 days after the earliest competitor.

Related data

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