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ToolMesh

Agent-native AI engineering lab.

Tagline: Prompts → Proof

What is ToolMesh?

Most engineering tooling and operating models were designed for humans. When the executor becomes an AI agent, teams run into the same failure modes again and again:

  • work is described in chat, not machine-readable contracts
  • changes come without replayable evidence (what happened, why it’s safe, how to verify)
  • pipelines don’t provide agent-first guardrails (least privilege, policy, audits, deterministic gates)

ToolMesh is our umbrella for experiments, open-source components, and productized delivery that make agents reliable at enterprise quality — by building glue around existing models and runtimes (not “new models”).

How we work (lab → product)

We ship small “test rigs” through the same agent-first flow and productize the reusable parts:

flowchart LR
  A["Work Contract (spec)"] --> B["Agent Execution (tools)"]
  B --> C["Receipts (evidence)"]
  C --> D["CI Replay/Verify"]
  D --> E["Ship + Observe"]
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Flagship wedge (v0)

ReceiptCI / Proofpack — a proof-carrying CI gate for agent work.

The idea: every agent-produced change ships with a machine-readable contract + receipts (inputs, tool calls, diffs, tests, evaluations) + a replay path that CI can deterministically verify.

Exploring next (shortlist)

  • AgentReplay — sanitized, replayable agent runs (private for teams; public for distribution).
  • SkillHub — verified skill bundles + compatibility matrix + auto-updates across runtimes.
  • A2A Handoff Standard — portable handoff artifacts (memory + contracts) across vendors.

Work with us

Agent-to-Prod Sprint (2–4 weeks): we help teams install an agent-first SDLC (contracts, receipts, quality gates) and deliver the first production use-case.

Showroom (projects)

Pitch

Landing

Contributing

If you want to collaborate, open an issue in any repository above.

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Agent-native AI engineering lab. Prompts → proof.

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