Make every agent build the way your organization builds.
Turn your architecture standards, conventions and institutional knowledge into rules every AI coding agent follows, and measure what the AI investment actually delivers.
Today, everyone has AI. The organization isn't faster.
With Mia-Platform, everyone has AI. The organization compounds it.
Every developer has AI, but each team uses agents differently. Your architecture standards, conventions and institutional knowledge are not available to those agents, so every agent improvises.
Every agent starts from the same codified standards, whichever coding assistant each team chose, so two teams working on the same problem converge instead of diverging.
Review becomes the bottleneck. Engineers spend their day redoing what an agent produced, and quality drifts release after release.
Quality bars run as evals and scorecards before a human looks at the change, so engineers move above the loop and approve work instead of redoing it.
Licence spend grows and leadership cannot show what it delivers. Trust in AI erodes while the bill does not.
AI-native SDLC indicators show what changed, team by team, so the board conversation is about outcomes instead of seats.
From standards nobody reads to rules every agent follows.
Four moves, in this order: what good looks like has to exist in a form an agent can read before anything can enforce it.
Codify
Standards, architecture decisions and institutional knowledge become governed context in Context Catalog, instead of living in wikis that agents cannot read.
Distribute
AI Playbooks fix the model, the skills and the MCP tools a task is allowed to use, so the same way of working reaches every team.
Enforce
Quality bars run as evals and scorecards, and separation of duties becomes review policy: the agent that writes the code cannot approve it.
Measure
AI-native SDLC indicators turn the investment into numbers you can show: what reaches production first time, and what still comes back.
What you put in place.
Four mechanisms, each one the answer to a different way standards fail to reach an agent.
Standards as governed context
Architecture decisions, conventions and institutional knowledge become catalogued items with an owner, a version and an access rule, so an agent reads the current answer rather than the loudest one.
AI Playbooks
Agentic workflows with model, skills and MCP tools fixed in advance and validated before they run, so what good looks like travels with the task instead of with the engineer.
Scorecards and compliance campaigns
Quality bars expressed as checks that run continuously across every team, and campaigns that drive the gaps to zero instead of reporting them once a quarter.
Separation of duties
Review policy enforced where the change happens: the agent that authored a change cannot be the one that approves it, in any repository or toolchain.
Why the usual answers stop short.
Measurement tools measure, and context tools provide context. Neither changes what the agent does next.
Each row is the same job: the answer most organizations reach for, and what changes here.
A dashboard reports how much AI your teams adopted, and how the numbers moved.
Enforced, not only measured
Your standards reach the agent while it works, and the dashboard measures the result of that.
A context tool makes your documents retrievable, so the agent has more to read.
Governed, not just retrievable
Catalogued context carries the owner, the version and the quality bar that goes with it.
More licences, and the hope that practices converge on their own.
Across every assistant
The same standards apply to every coding agent your teams already pay for.
Standards that stop at the edge of the SDLC.
One set of rules
The same standards then govern the business agents your organization puts in production.
What you will be able to measure.
The AI-native SDLC indicators, on your own baseline. These are the numbers that tell you whether the standards reached the agents, and whether that changed the outcome.
Leading
What moves first, within weeks.
- Time to committed artifact
- First-pass CI success rate
Lagging
What confirms the change held.
- Escaped defects
- PR cycle time
- First-pass survival rate
Adoption
How far the standards actually reached.
- Share of agents working from the shared standards
- Share of teams on the same playbooks
We publish the indicators, not an average improvement: the baseline is yours, and so is the delta. Measuring them before anything changes is part of the work.
What makes it work.
Three parts of the platform carry this use case. The rest of it is there when you need it.
Context Catalog
Holds the standards, decisions and knowledge agents read, with scorecards and campaigns that keep them honest.
AI Foundry
Turns a way of working into an AI Playbook with model, skills and MCP tools fixed before it runs.
Console
Carries the golden paths where those standards get applied, from project creation to deploy.
The objections this raises, answered.
No. Mia-Platform does not replace coding assistants, and it is not one. Your teams keep the assistants they already pay for; what changes is that all of them work from the same standards, the same context and the same quality bars, so the licences you already bought start compounding.
Where this goes next.
Run the AI-Native SDLC
Agents working through every stage of delivery, under guardrails your platform team controls.
ExplorePilot to Production
Move the business AI initiative you already believe in to a governed production service.
ExploreAI Governance
Control AI spend, risk and compliance at the point where AI runs.
Explore