Coorva

For engineering teams using AI coding agents

Delegate More Engineering Work to AI Without Losing Control

Coorva AI-SDLC defines what AI can do, the evidence it must produce, and where human accountability remains.

  1. Readiness
  2. Exposure
  3. Autonomy
  4. Evidence
  5. Ownership
Start an AI-SDLC ImplementationSee How Metis Works

Keep Jira, GitHub, CI/CD and the agents you already use.

AI solved one bottleneck. It may be creating another.

AI Can Generate More Than Your Team Can Safely Review

More output creates leverage. Then review, context and accountability become the new bottleneck.

More code. Same review capacity.

Agents can generate work faster than senior engineers can meaningfully validate every change.

When everything needs the same manual attention, review becomes the bottleneck.

More artifacts. Not necessarily more control.

Specs, plans, traces, tests and PRs only help if engineers can quickly see what matters.

More information does not automatically mean better governance.

AI can fill the gaps.

Missing acceptance criteria, undocumented rules and hidden constraints can become decisions the agent makes on its own.

The real risk is not realizing a decision was made.

Not every change deserves the same autonomy.

A reversible refactor and a production data migration do not carry the same consequences.

The key question is not: “Can the agent do it?”

It is: “What happens if the agent is wrong?”

The goal isn’t less AI. It’s the right level of control for each decision.

Introducing Risk-First AI-SDLC

Let AI Do More. Make the Rules of Delegation Explicit.

Coorva AI-SDLC defines how much autonomy AI gets, what controls each change requires, and where human accountability remains.

How much control does this change require?

That decision drives the operating model.

  1. Step 01

    Establish Context

    Give the lifecycle access to the project vision, technical guardrails, architecture, security requirements, testing standards and existing engineering decisions.

    So the agent does not begin from a blank prompt.

    It begins inside your engineering context.

  2. Step 02

    Check Readiness

    Before execution starts, determine whether the Work Item contains enough information to delegate responsibly.

    • Is the goal clear?
    • Is the expected outcome explicit?
    • Are acceptance criteria verifiable?
    • Are dependencies, constraints and material unknowns visible?
    If not:Needs ClarificationIf yes:Ready
  3. Step 03

    Assess Exposure

    Now ask what could happen if the implementation is wrong.

    Evaluate factors such as:

    • impact
    • blast radius
    • data
    • security
    • reversibility
    • availability
    • architecture
    • uncertainty

    Not to produce a decorative risk score.

    To decide how the work should be controlled.

  4. Step 04

    Set the Autonomy Policy

    Translate exposure into rules the agent can actually follow.

    Define:

    • what it may modify;
    • what it may not modify;
    • which approvals are required;
    • which tests must pass;
    • who must review;
    • whether auto-merge is allowed;
    • what rollout conditions apply.
  5. Step 05

    Let the Agent Execute

    Claude, Codex or another coding agent performs the implementation.

    Metis does not need to become another coding agent.

    It governs the conditions under which the agent works.

  6. Step 06

    Require Evidence

    Before the change can move forward, compare what evidence was required with what was actually obtained.

    • Tests.
    • CI.
    • Static analysis.
    • Security checks.
    • Acceptance criteria.
    • Data changes.
    • Known limitations.
    • Reviews.

    The question is no longer:

    “Did the agent say it worked?”

    It becomes:

    “What evidence do we have that this change is acceptable?”

  7. Step 07

    Keep the Decision Human

    The accountable owner sees the context, exposure, policy, available evidence and known trade-offs.

    Then explicitly:

    Approve.Request Changes.Reject.

    Metis records who decided, when they decided and what they knowingly accepted.

This isn’t about producing more AI output.

Make More AI Output Governable

Delegate more without losing accountability

Give AI more autonomy where risk is lower, and more control where consequences are higher.

Focus senior attention where it matters

Surface the work that needs clarification, evidence or approval instead of reviewing every change equally.

Catch ambiguity before it becomes code

Expose missing rules and acceptance criteria before AI turns assumptions into implementation.

Decide from evidence, not confidence

Require the proof that matters before work moves forward.

Keep every decision traceable

Follow each Work Item from intent through implementation, evidence and approval.

Keep your existing engineering stack

Add governance around Jira, GitHub, CI/CD and the coding agents your team already uses.

Keep human ownership explicit

AI can execute more without becoming accountable for material engineering decisions.

Make it clear who reviewed, who approved and what trade-offs were accepted.

ReviewedApprovedTrade-offs accepted

AI-SDLC is not another tool in the same category

You Don’t Need Another Agent. You Need Better Control.

Coding assistants help AI execute.

Coorva governs the delegation.

Claude, Codex and other coding agents can write and modify software.

Metis helps determine the conditions under which that work should be accepted.

Spec-driven development structures the work.

Risk-First determines the control around the work.

A specification can tell an agent what to build.

It does not automatically answer:

  • Is this ready?
  • How dangerous is an error?
  • What can the agent touch?
  • What proof is enough?
  • Who accepts the residual risk?

Generic AI governance controls AI usage.

Metis governs AI software delivery.

Enterprise AI governance often focuses on:

models, access, privacy, usage and spend.

Metis focuses on:

engineering decisions inside the SDLC.

“Human in the loop” tells you that a person exists somewhere.

Risk-First tells you why, when and where that person is required.

A low-exposure change may need little human intervention.

A high-exposure change may still be implemented by AI, but require strict boundaries, stronger evidence, multiple reviewers and controlled rollout.

Risk Doesn’t Decide Whether AI Can Work.It Decides How AI Is Allowed to Work.

Coorva Metis

See the Whole Governance Decision Without Reading Every Artifact the Agent Produced

  • Is it ready?

    What is clear?

    What is still missing?

    Who decided it was ready?

  • What could go wrong?

    What data is affected?

    What is the blast radius?

    How reversible is it?

    Which assumptions still exist?

  • What is AI allowed to do?

    Which components may it modify?

    Which areas are off-limits?

    Which approvals are mandatory?

    Is auto-merge allowed?

  • What evidence do we have?

    Which checks passed?

    Which acceptance criteria were verified?

    What is still pending?

    Which limitations remain?

  • Who owns the final decision?

    Not “the agent”.

    Not “the pipeline”.

    A named person.

    With authority over the decision.

Metis brings Context, Readiness, Exposure, Autonomy, Evidence and Decision into one governance view.

See what needs attention, then drill into Jira, GitHub or the repository when needed.

Metis adds the decision layer across your existing engineering stack.

Start an AI-SDLC Implementation

Start with one real engineering workstream

Implement AI-SDLC Without Redesigning Your Entire Engineering Organization

Apply Risk-First AI-SDLC to a bounded workstream inside your existing stack. Define what is ready to delegate, how much autonomy AI gets, what evidence is required, and where human ownership remains.

Keep Jira, GitHub, CI/CD and the coding agents you already use.

Your AI-SDLC implementation establishes

  • Project context & guardrails

    Make the rules AI must work within explicit.

  • Readiness criteria

    Define when work is clear enough to delegate.

  • Risk-First exposure

    Make the consequences of failure visible.

  • Autonomy policies

    Turn exposure into permissions, approvals and controls.

  • Evidence requirements

    Define what must be proven before acceptance.

  • Human ownership

    Make the final engineering decision explicit and traceable.

Start bounded. Build from evidence.

Begin with one real, clearly bounded workstream. Establish how AI-assisted work should be governed in your environment, observe the model on real engineering work, then decide where broader adoption makes sense.

Use a real workstream. Keep your existing stack. Build the controls required to move AI-assisted work forward.

Start with one implementation before deciding how far to scale.

Start bounded. Keep control.

Start AI-SDLC Without Betting Your Entire SDLC

Apply AI-SDLC to one real workstream while keeping your existing engineering stack and ownership.

You keep:

  • your backlog
  • your repositories
  • your coding agents
  • your CI/CD
  • your engineering ownership

Coorva adds the governance layer around the work, not another parallel system.

Implementation Clarity Commitment

Your first implementation defines what is Ready, how Exposure is assessed, how AI autonomy is constrained, what evidence is required, and who owns the final decision.

Apply the model to real engineering work before deciding where broader adoption makes sense.

Evidence first. No lock-in.

  1. Start with one workstream
  2. Observe it on real engineering work
  3. Decide where to expand

Built from engineering practice, not theory

We Hit the Same AI-SDLC Problem We’re Helping Teams Solve

AI made it easy to produce more specs, tasks, code and evidence. But more output did not automatically make the work easier to understand, review or govern.

How do we delegate more engineering work to AI without making human review the next bottleneck?

That question became the foundation of Risk-First AI-SDLC and Metis.

The PoC is explicitly measuring factors including:

  • how much work is implemented by AI;
  • human intervention during implementation;
  • time from Ready to PR;
  • rework;
  • review time;
  • material decisions and escalations;
  • defects and incidents;
  • whether sufficient evidence exists for acceptance.

AI-SDLC is still being validated against outcomes such as review burden, rework and defects.

What exists today is a concrete lifecycle, governance model and platform designed to make AI-assisted engineering decisions explicit and visible.

The agents will keep getting better

The Advantage Is Knowing What to Delegate and What Still Requires Human Judgment

AI can execute more of the software lifecycle.

Coorva AI-SDLC makes the rules explicit: when work is ready, how much autonomy AI gets, what evidence is required, and where human ownership remains.

Delegate more. Keep control of the decisions that matter.

Let AI Do More.Keep Engineers Accountable for What Matters.

Start an AI-SDLC Implementation

Bring one real engineering workstream.

Keep your Jira, GitHub, CI/CD and coding agents. Add the Risk-First governance needed to delegate AI-assisted work with explicit controls and human accountability.

  1. Readiness
  2. Exposure
  3. Autonomy
  4. Evidence
  5. Ownership