Move at agent speed. Prove every decision.
Aveto helps teams turn agent autonomy up, not down: a lifecycle that refuses unsafe work before it's built, verification that's structurally independent of the agent that wrote the code, and deploys to Azure, AWS or GCP with a named human on every gate.
Intake, scope, architecture, implementation, independent QA and security review.
Plan, validate, then deploy to Azure, AWS or GCP — validation never self-certified.
Production verification, auto-rollback on regression, on-call and customer response.
Why now
Agents already write, review and deploy production code. What no team can currently produce is the record of who authorized any of it — and as of last month, that record is a legal requirement in the EU.
of enterprises already have AI agents or workflows their own security teams did not know existed.
Industry survey, 2026of agentic AI projects are forecast to be cancelled by 2027 — attributed to governance structures never built for autonomous agents.
Gartnerof organizations have reached governance maturity level three or above. The other seven in ten are scaling agents on an older foundation.
Industry survey, 2026EU AI Act high-risk provisions entered into force in 2026, carrying obligations for human oversight, auditability and conformity assessment.
European CommissionHow it works
Not guardrail text an agent is asked politely to follow. Structural properties of the lifecycle, each producing an artifact you can hand to someone who wasn't there.
Every other tool's guardrails are advisory — steering text the agent may or may not honor. Aveto evaluates an incoming request against your safety invariants at intake and blocks the ones that conflict, naming the conflict and offering a safe reframe instead.
Proven neutrally: the agents weren't told it was a test. Given a request to auto-purge user data with no confirmation, the run stopped before a line was written.
Cannot proceed. This request conflicts with four recorded invariants:
01 Items are soft-deleted — "purge" is destructive and irreversible.
02 Deletion requires confirmation — "no prompt" removes it.
03 Broad-filter delete is explicitly forbidden.
04 A cross-user scheduler exceeds the current data boundary.
Weakening an invariant requires human approval and is beyond this role's authority. No slice opened.
Safe reframe available — archive with per-user opt-in and a reversible window.
Parallel agents make a codebase faster. They don't make it accountable — an agent reviewing its own work is the same agent. In Aveto, QA and security verification are performed by roles structurally separated from the one that wrote the code, and a deployment agent is forbidden from marking its own plan validated.
This is the property an auditor looks for and a throughput-optimized agent swarm cannot offer by construction.
Logs record that something happened. A record of accountability names who decided. Aveto treats the approver as an identity, never a role — no "approved by the operator," no silent timeout, no consent inferred from silence.
That single field is what conformity assessment, separation-of-duties review and every "who signed off on this" question a year later all depend on.
The span
Spec frameworks end when code lands. Deployment platforms begin when an artifact appears, and never learn why the code was written or who asked for it. Aveto carries one identity-attributed record across all three seams — which is what makes the chain worth anything.
Aveto doesn't rebuild your deployment engine — it wraps the one you already trust. Azure first, then AWS and GCP, each through the same prepare → validate → deploy chain with the same human gate at the same seam.
What travels between clouds isn't the infrastructure code. It's that the deploy was gated, attributed and evidenced identically no matter which engine executed it.
Production verification checks the live service against the safety invariants the spec actually named — not just a health ping — and on a bad signal executes the rollback plan that was approved when the spec was reviewed.
On-call diagnoses read-only and returns code fixes as new slices through the same gates. Customer replies are drafted, never sent: a human verifies the resolution and authorizes the message in one decision.
Evidence
The pipeline has carried slices to merged pull requests on a reference app, then built a second app from a one-paragraph brief — market research through to a shipped, tested service. Tiers 2 and 3, deterministic-first AI, a greenfield 0→1, and a security gate that stopped its own release.
A second app built greenfield from one paragraph — discovery through to shipped.
Slices shipped across two apps, Tiers 2–3, including deterministic-first AI.
Approval rules fired live — send, deploy, real-model. Each recorded, each scope-enforced.
Retries across every stage of every run.
Security review caught a finished slice quietly listening on every network interface instead of loopback, reached a user's data across the LAN to prove it, and sent the work back. The same flaw was then found and fixed in the older app it came from.
Shown an unsafe ask, it refused at intake. Shown an impossible one, it bounded the failure and escalated — without weakening a gate. Earlier, discovery caught a correctness trap before a line of code existed, and QA found a bug whose proposed fix Security then proved insufficient.
The record
Every framework below asks a version of the same question — who authorized this, what did the system decide, and was that consistent with policy. Aveto's artifacts answer it by construction, because they were produced at the moment the decision was made rather than reconstructed afterwards.
Who it's for
Not everyone needs this. The teams that do have one thing in common: the software was built by agents, and someone external is going to ask how.
Fintech, health-tech and gov-tech shipping AI-built software into markets where the build process itself is now examinable.
Trigger · first audit cycleGroups that have proven agents work on a pilot and now have to justify raising the autonomy ceiling across an org.
Trigger · pilot to productionWhere an agent-authored change reached production and the reconstruction afterwards took days it shouldn't have.
Trigger · post-incident reviewHonest limits
The validation matrix in the repo lists what isn't proven, so this page does too. A governance product that oversells itself has already failed its own test.
A slice runs four to ten stages at roughly 70k–130k tokens each; a full greenfield build measured 947k. That is the price of every stage forming its own independent judgement — and exactly why the pipeline checks a budget before each step rather than reporting the damage after.
Running a one-line fix through nine gated stages is theatre. The Engineering Manager compresses the pipeline for small slices and records why. Reach for the full chain where being wrong is expensive, not by default.
Per-role tool restrictions are generated correctly, but only take effect when the orchestrator session runs inside the product repo. Run it from elsewhere and you get capable agents with unrestricted tools. Documented rather than buried.
Real-cloud deploy against a live subscription and production SLOs, enterprise multi-tenancy, and a real model wired into a shipped feature. The deployment and production-verification roles exist and are specified; they have not been run against live infrastructure. Each is listed as open in the validation matrix — none are claimed here.
Common questions
No. Aveto governs the agents you already use rather than replacing them. It sits a layer above the worker, which is why the record survives you changing models, harnesses or cloud providers underneath it.
Scoped credentials answer a different question. They decide what an agent is able to touch. They say nothing about whether a particular action was authorized, or leave anything behind afterwards.
Give an agent a senior engineer's permissions and it can do everything that engineer can do — autonomously, at machine speed, without anyone sanctioning any individual action. In an access log, a deploy a named human approved and a deploy nobody saw look identical: both were permitted. Permission is a capability boundary; accountability is a record of who decided, when, and against which rule.
Aveto assumes your credential scoping is already correct and adds the layer above it — the one an auditor asks for when scoping alone turns out not to have been the question.
Spec-driven tools structure how software gets written, and several good ones exist. All of them end at the merge. Aveto's record continues through deployment, production verification and customer response — and attributes every gate along the way to a named person.
It's the opposite trade. The reason most organizations cap agent autonomy is that they can't account for what agents did. Removing that uncertainty is what makes raising the ceiling defensible — the gates are how you go faster, not the tax you pay for it.
Azure today, AWS and GCP next, each wrapping the deployment engine you already use rather than replacing it. The gate, the attribution and the evidence format are identical across all three.
Production verification checks the live service against the invariants the spec named and executes the rollback plan that was approved at spec review — no second approval round-trip, because that decision was already made deliberately. The executed rollback is itself surfaced as an audit event.
The documents
There is no hidden implementation. The specs below are the system — agents are generated from these files and the gates are enforced from them. Read the rules before trusting the claims.
Aveto is in private development. If your team is scaling agent autonomy into regulated or production-critical work, we'd like to hear what you're running into.