AI Engineering Governance for Teams Using Coding Agents

Give AI More Autonomy. Keep Engineering in Control.

Metis makes it clear what is ready to delegate, what agents are allowed to do, what evidence is required, and who owns the final decision.

Keep Jira, GitHub, CI/CD and the coding agents your team already uses.

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One Work Item. Five governance decisions.

Metis / Work Items / BILL-142

BILL-142

Migrate existing invoices to multi-currency schema

Readiness
Exposure
Autonomy
Evidence
Ownership

Readiness

Ready

Goals defined
Acceptance criteria written
Dependencies identified

Exposure

High

Consequence of failure: High
Uncertainty: Medium
Reversibility: Low

Autonomy Policy

Supervised

Permissions: Draft implementation
Restrictions: No direct production changes

Evidence

6 of 9

CI checks passed
Migration review

Human Decision

Pending

The named owner reviews exposure, evidence and accepted trade-offs before the final decision.

AI is changing how software gets built

AI Output Is Scaling. Engineering Review Still Has Limits.

Coding agents can produce more code, tests and Pull Requests. But generating work and deciding whether it is acceptable are two different problems.

More code. Same review capacity.
Agents can produce changes faster than senior engineers can meaningfully validate every decision.
Missing context becomes implementation.
Unclear requirements or undocumented constraints can become choices the agent makes without explicit human direction.
Different exposure. Same review process.
A reversible refactor and a production data migration should not operate under identical controls.
Evidence scattered across tools.
Specifications, PRs, checks and approvals live in different places. Understanding the full decision takes effort.

The real question is not just whether an agent can do the work. It is what must be true before that work can move forward.

Meet Coorva Metis

Make the Rules of AI Delegation Explicit.

Metis adds a governance layer around your existing software delivery workflow.

For every Work Item, it brings five decisions into focus.

BILL-142

Migrate existing invoices to multi-currency schema

ReadinessReady
ExposureHigh
AutonomySupervised
Evidence6 of 9
OwnershipPending

Readiness

Ready

Goals defined
Acceptance criteria written
Dependencies identified
Constraints documented

Risk determines the controls around AI execution, not whether AI can participate.

More AI execution should not mean less engineering control

Delegate More Work. Make Better-Informed Decisions.

Give agents more room to execute
Set autonomy according to the exposure of each change, with tighter controls where consequences are higher.
Focus human judgment where it matters
See which Work Items need clarification, additional validation or human approval.
Catch ambiguity before it becomes code
Make missing requirements and constraints visible before implementation begins.
Decide from evidence, not confidence
Know which checks are required, which are complete and what is still missing.
Keep every decision connected to the work
See context, execution, evidence and human ownership in one governance view.

Purpose-built for AI-assisted engineering

Not Another Agent. The Governance Layer Around Your Agents.

Your agents execute. Metis governs.
Coding agents implement changes. Metis makes the conditions for delegating and accepting that work explicit.
Your engineering tools stay in place.
Jira manages the backlog. GitHub manages code and Pull Requests. CI/CD runs technical checks. Metis connects the information needed for governance decisions.
Human accountability stays visible.
A named owner can review the exposure, evidence and accepted trade-offs before making the final decision.

Risk doesn't decide whether AI can work. It decides how AI is allowed to work.

Governance should be visible

Governance Decisions, Visible in the Work Item.

Metis turns scattered engineering signals into a clear decision view.

Instead of searching across tickets, Pull Requests, CI results and comments, engineering leaders can see what is ready, what carries higher exposure, what AI is allowed to do, what evidence exists and what still requires human approval.

Not a replacement for engineering judgment. A clearer foundation for it.

One Work Item. One Governance View.

Example: Migrate existing invoices to a multi-currency schema

See what is ready, what requires attention, and what still needs approval.

Illustrative Metis product view using sample data.

See Metis applied to real engineering workflows

Evaluate How Metis Could Fit Your AI Delivery Process.

In a focused walkthrough, we will show how Metis helps engineering leaders see readiness, exposure, autonomy, evidence and ownership in one place.

Come with a real workflow, or an AI adoption challenge your team is working through.

Built for engineering teams adopting AI coding agents with increasing autonomy.