Why “Done” Must Be a Trust Protocol for AI Agents

Dotta explains that true “done” for AI agents means a verified artifact meets a standard, carries evidence, assigns risk, and authorizes the next step, creating a trustworthy protocol.

Ever wonder why “done” feels like a moving target when AI agents crank out work faster than you can check? In this talk, Dotta flips the script on how we define completion.

⚡ TL;DR: Dotta shows that “done” must mean an artifact meets a clear standard, carries evidence, is verified, assigns risk, and authorizes the next step — turning vague confidence into a trustworthy protocol.

The Simple Version

Think of “done” like a signed receipt. The artifact proves it met the rules, the verifier checks the receipt, and the next action knows who owns the remaining risk.

How It Actually Works

Trust Protocol Foundations

Here the protocol defines what “done” looks like and how it’s recorded.

🎯 Think of it this way: It’s like a recipe that lists every ingredient, cooking time, and who tasted it.

Risk‑Based Review Routing

Instead of checking everything, the system routes reviews to the right verifier based on how risky the artifact is.

🎯 Think of it this way: A fire alarm gets immediate attention, while a flickering light might wait.

Liveness Model in Action

The liveness model ties completion to a living state where the artifact can trigger the next step safely.

🎯 Think of it this way: A door unlocks only when the lock confirms the key is valid.

Technical Deep Dive

Artifacts carry metadata that states the standard they satisfied.

Verifiers consume this metadata to emit a pass/fail signal.

Ownership tags assign a responsible party for any residual risk.

Authorization fields embed a next‑action command that downstream agents can execute.

🎯 Think of it this way: It's like a CI pipeline that not only builds code but also signs the binary before deployment.

Why This Matters

Teams can avoid endless approval loops and keep work flowing.

Risk‑aware routing reduces wasted human effort.

Clear ownership lets engineers trust AI output enough to build on it.

Overall, the model turns vague confidence into a reproducible trust contract.

Key Takeaways

“Done” must include a clear standard and evidence.
Verification should be delegated to the right verifier.
Risk ownership must be explicit.
Next‑action authorization enables chaining.
The liveness model ties completion to safe continuation.
Trust protocols replace approval theater.
Scaling AI teams rely on this protocol for safety.

Why “Done” Must Be a Trust Protocol for AI Agents – Dotta Explains