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A Coorva product

Metis

More autonomy for AI agents, with engineering decisions kept under control.

Metis brings together in one view which tasks are ready to delegate, what agents can do, what evidence is needed, and who is responsible for approving the result.

It integrates with the development process and the tools your team already uses.

More AI-driven execution requires better engineering decisions

AI agents can generate code, tests, and pull requests. But producing a change and deciding whether it’s ready to accept are two different problems.

As agent-generated work increases, it becomes even more important to review requirements, risks, and the available evidence.

  • More changes to review

    Agents can produce work faster than a team can review it thoroughly.

  • Insufficient context

    Ambiguous requirements or unclear constraints can turn into decisions made during implementation.

  • Different levels of risk

    A small, reversible change does not need the same controls as a data migration or a security-related modification.

  • Scattered evidence

    Specifications, pull requests, validations, and approvals are often spread across different tools.

Five decisions to govern every task

Metis organizes the decisions that determine when work can be delegated to AI, under which conditions, and what must be verified before accepting the result.

  1. 01

    Readiness

    Readiness

    Is this task ready to delegate?

    Helps review objectives, acceptance criteria, dependencies, and constraints before work begins.

  2. 02

    Exposure

    Exposure

    What would happen if something went wrong?

    Helps assess the impact of a change, its uncertainty, and the possibility of reverting it.

  3. 03

    Autonomy

    Autonomy

    What can the agent do?

    Makes permissions, restrictions, required reviews, and approval conditions explicit.

  4. 04

    Evidence

    Evidence

    What do we need to verify?

    Helps compare required validations with the available evidence before accepting the change.

  5. 05

    Ownership

    Ownership

    Who makes the final decision?

    Keeps visible who must review, approve, and take responsibility for important decisions.

Risk does not determine whether AI can participate. It determines the controls under which AI can do so.

One view to understand every decision

Metis brings together a task’s status, exposure, autonomy conditions, pending validations, and human accountability.

This helps the team identify what needs attention without reconstructing context across multiple tools.

Governance view by Work Item. Illustrative Metis view with sample data.
Metis screenshot showing the governance view of a Work Item: current status, risk, pre-merge validation, items that require human attention, lifecycle, autonomy, evidence, AI usage, and sources. The interface shown is in Spanish.

Information is presented in a concise format, with the option to explore the original sources when needed.

Visibility into AI agent usage and activity

Metis also provides a shared view of token usage, estimated costs, AI agent activity, and sessions.

This helps teams understand where AI resources are being used without having to inspect each session individually.

  • Token usage
  • Estimated costs
  • Agent activity
  • Sessions
AI usage and activity dashboard. Illustrative Metis view with sample data.
Metis screenshot showing the AI usage dashboard: token usage by type, total tokens, estimated cost, sessions, daily tokens, estimated cost distribution, and tool activity. The interface shown is in Spanish.

The costs shown are estimates, not invoiced amounts. Token usage alone is not a measure of engineering productivity.

Your existing tools stay in place

Metis adds a governance layer on top of the existing development process.

Jira continues to manage the backlog. GitHub keeps the code and pull requests. CI/CD runs technical checks, and agents continue executing delegated work.

Metis connects the information needed to make engineering decisions more visible.

  • Backlog

    Tasks, priorities, and statuses remain in the team’s management tools, such as Jira.

  • Code and pull requests

    Repositories, changes, and technical reviews remain in tools such as GitHub.

  • Validations

    CI/CD processes continue running tests and technical checks.

  • AI agents

    Existing agents carry out implementation work. Metis adds visibility into governance conditions and decisions.

Agents execute. Metis makes the conditions for delegating and accepting work visible.

Metis and AI-SDLC serve different purposes

At Coorva, we work both on implementing AI-assisted development processes and on tools to govern that work.

Service

AI-SDLC implementation

An engineering service for introducing AI agents into the development process, defining working practices, and establishing controls and responsibilities.

Explore the service

Product

Metis

A Coorva product that helps teams visualize and organize governance decisions and gain observability into AI-assisted development.

These are different offerings that can complement each other, but neither automatically requires purchasing the other.

Want to learn more about Metis?

If you have a question about the product or want to learn how it could apply to your team’s work, get in touch.

Get in touch