The control layer for production AI agents

Agent infrastructure,
built for trust.

Versioning, replayability, audit, and portability for AI agents.

Don't trust the agent. Verify its genome.

agent.yaml — agenomic

// Platform

One control layer for AI agents.

01

Version

Track what changed between agent releases.

02

Replay

Re-run critical decisions with the same genome.

03

Govern

Enforce policies before agents act.

04

Prove

Export signed evidence for audits and incidents.

// The problem

Agents change. Logs don't explain why.

Prompts change. Models update. Tools evolve. Memory mutates. Policies drift.

Traditional logs show events. They don't prove what changed, whether behavior can be replayed, or which release caused the risk.

Before

scattered logs
screenshots
unknown behavior

After

signed genome
trace ledger
replay report
evidence package
replay_report.jsonMATCH
events replayed   12,418
divergences       0
fidelity           100.0%
baseline  sha256:4f8c2ab9

// The genome

The agent genome.

One signed record of everything that shapes agent behavior.

promptsmodelstoolspermissionspoliciesdependenciesmemory contractsexecution tracesbehavioral fingerprintscompliance evidence
agent.genome
agent.genome
├─ prompts
├─ models
├─ tools
├─ policies
├─ traces
├─ metrics
└─ evidence
evidence_package.zipsigned
├─ technical_docs.pdf
├─ trace_ledger.jsonl
├─ risk_assessment.md
├─ policy_results.json
└─ signature.sig
✓ sha256 verified

// Compliance

EU AI Act evidence, by design.

Generate technical evidence for risk management, documentation, record-keeping, and human oversight.

Agenomic provides technical evidence. It does not issue legal certification. Legal compliance requires qualified review.

Article 9

Risk evidence per release.

Article 11

Technical documentation from the genome.

Article 12

Signed traces and audit records.

Article 14

Human oversight proof when required.

compliance_statusrelease 4f8c2ab9
Art. 9  Risk management● ready
Art. 11 Technical docs● ready
Art. 12 Record-keeping● ready
Art. 14 Human oversight○ review
// technical evidence — not legal certification

// Workflow

Start local. Govern in the cloud.

CLI
$ agenomic init genome.yaml
$ agenomic run agent.yaml
$ agenomic diff baseline.lock candidate.lock
SDK
from agenomic import Client
 
client = Client(api_key="agenomic_dev_key")
client.runs.create(pipeline="agent.yaml")
Cloud
$ agenomic cloud push-agent out/bundle
$ agenomic cloud replay latest
$ agenomic cloud evidence latest

// MCP server

Governance your agents can call.

With the Agenomic MCP Server, agents can write their own evidence trail while they run.

Agenomic becomes the control layer agents use directly.

agenomic-mcp · tools
start_run
append_event
evaluate_policy
request_human_review
verify_run
generate_evidence_package

// Use cases

Built for production agent teams.

Release governance
Incident replay
Security reviews
Compliance evidence
Agent regression testing
Human oversight
Behavioral drift detection
Environment migration

Make every agent release provable.

Version behavior. Replay decisions. Export evidence. Govern production agents.

Create your first genome
AI agent versioning, governance & EU AI Act compliance