Run the AI-native SDLC on a foundation you own.
Agents working through every stage of delivery, from plan to maintain, under guardrails your platform team controls, whichever coding agent or toolchain each team uses.
Today, build is fast. Delivery isn't.
With Mia-Platform, build is fast. So is everything around it.
Build is no longer the constraint. Agents write code at machine speed, while planning, testing, review, deployment and incident handling still run at human speed.
Each stage hands the next one a versioned artifact instead of a conversation, so the steps around build stop being the place where the speed is lost.
Agent configuration, MCP access and permissions are set per engineer. Nobody can say what the agents in the organization are currently allowed to do.
Configuration, guidance and MCP access are administered centrally, so one guardrail changed once applies at once to every agent, across toolchains.
There are no consistent autonomy rules between dev and production, and the speed gained in build is paid back as lock-in to whichever toolchain vendor arrives first.
Autonomy is scoped to the environment and the platform stays neutral: the same rules run on top of the pipelines and assistants each team already uses.
One loop, six stages, the same guardrails in each.
Agents work inside the loop your platform team defines. What moves between stages is a versioned artifact, and that artifact is the audit trail.
Plan
A backlog item becomes a versioned spec, produced in read-only mode and approved by a human before anything is written.
Design
The approved spec becomes a plan that carries the architecture decisions and standards it has to respect.
Build
Coding agents pick up the work with configuration and MCP access managed by the platform team, not by each engineer.
Test
Automated checks and eval gates run before any human review, so a person sees a change that already passed the bar.
Deploy
Autonomy is scoped to the environment, and the agent that authored the change cannot be the one that approves it.
Maintain
An incident triggers a remediation agent with short-lived scoped credentials, and its fix re-enters the loop as a new plan.
What the platform team controls.
Four controls, administered in one place and applied wherever the agents happen to run.
Versioned artifacts between stages
Spec, plan, change and fix exist as artifacts with a version, not as chat history. The trail from the ticket to the deploy is the by-product of running the loop, so an audit does not need a reconstruction.
Central agent configuration and MCP access
Which agents exist, what guidance they carry and which MCP servers they may reach is administered in one place. Change a guardrail there and every agent picks it up, whatever toolchain it runs in.
Environment-scoped autonomy and separation of duties
Open in dev, intermediate in staging, gated in production. At the merge and deploy gates the authoring identity cannot be the approving one, so an agent cannot promote its own work to production.
SDLC automation
Vulnerability auto-fixes and self-healing incidents run as part of the loop, with their findings re-entering as plans to approve rather than as changes that appear in production unannounced.
Why the usual answers stop short.
The AI-native SDLC, with any agent and any toolchain, enforced by the platform you own.
Each row is the same job: the answer most organizations reach for, and what changes here.
The agent loop that comes with your pipeline vendor, governed inside that vendor's own toolchain.
Neutral by design
One playbook, run consistently across every coding agent and every toolchain your teams use.
A portal that catalogues what exists and scores how healthy it is.
Enforced, not catalogued
Rules reach the agent while it runs, instead of describing it on a page somebody has to read.
A foundation that stops at the edge of software delivery.
One foundation
The platform that runs your SDLC also runs the business agents your organization puts in production.
A US consumer-lending group operating across 25 states built its Internal Developer Platform on Mia-Platform Console, run as a product with its own team and golden paths, on the self-hosted infrastructure it already owned.
Reported by the customer
4 months
to integrate the platform with existing infrastructure
What you will be able to measure.
The AI-native SDLC indicators on the delivery side, on your own baseline.
Leading
What moves first, within weeks.
- First-pass CI success rate
- Time from anomaly to triage
- Share of findings merged
Lagging
What confirms the change held.
- PR cycle time
- Repeat incidents
Adoption
How much of the estate is actually governed.
- Share of agents under central configuration
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.
Console
Runs the golden paths, the environments and the deploy gates the loop passes through.
AI Foundry
Holds the agents, their Playbooks and their MCP access, administered centrally instead of per engineer.
Context Catalog
Gives every stage the same verified picture of the estate, and the standards each change has to respect.
The objections this raises, answered.
No. It does not replace CI/CD, coding assistants or systems of record. The pipelines you run keep running; what changes is that the agents working around them follow the same guardrails, in every environment and in every toolchain.
Where this goes next.
Scale Agentic Engineering
Turn your standards and knowledge into rules every AI agent follows, and prove the investment pays off.
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