K9 Audit
Causal intent-execution audit trail for AI agents
Details
- External ID
- 47344702
- Source
- HN
- Company
- —
- Product
- K9 Audit
- Website domain
- github.com
- Launched
- March 12, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.1070110701107011
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
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
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Observability & eval
- Audience
- B2B
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- audit trail for ai agent decision making
- Manually corrected
- False
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 — 141 launches clear the similarity bar, closest 8 shown.
Attention rank: #128 of 142 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 107 days after the earliest competitor.
- Continue · hn · 2026-02-17 · 44 upvotes · similarity 0.45
- Inficy · ph · 2026-09-21 · 1 upvotes · similarity 0.44
- Shield KYA · ph · 2026-09-21 · 1 upvotes · similarity 0.44
- Velt Activity Logs: Audit trail for humans and AI agents · yc · 2026-04-02 · 5 upvotes · similarity 0.43
- Policy enforcement for Claude Code, Cursor, and Codex · hn · 2026-07-09 · 13 upvotes · similarity 0.42
- Logira · hn · 2026-03-01 · 26 upvotes · similarity 0.41
- Grepathy · hn · 2026-07-15 · 18 upvotes · similarity 0.40
- Git why · hn · 2026-04-11 · 11 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
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