How the Power/Timing Impact Estimator Mission Works

When Ajay describes the Power/Timing Impact Estimator in Why It Matters, he calls it "one deceptively simple thing." My job is the deceptively part. Forecasting a change's resource cost at PR time is simple to state and involved to do correctly, because the honest answer is never in one place. It's spread across the diff, the system's resource budgets, the history of similar changes, and the specific hot paths a given subsystem cares about. Getting a trustworthy number means pulling those together in the right order — and being able to show your work.

So we didn't build a script. We built a StudioX Mission: a small org chart of specialist agents, coordinated by a reasoning core, that reasons about a goal, delegates to the right specialist for each part, and returns a result with every decision traced. Let me walk the architecture.

The agents in the mission

A Mission is a roster of agents, each an expert in exactly one thing, each backed by its own bot and its own knowledge base so it never reaches outside its lane. For the estimator, the roster is:

• Diff Analysis Agent — reads the PR. It reaches the source system through an MCP server and runs the static analysis: which symbols changed, what got added to hot paths, allocation sites, stack growth, new work inside interrupt context.
• Budget Agent — owns the posture. Its knowledge base holds the resource budgets: the CPU headroom per subsystem, the heap ceiling, the ISR deadline and the current worst-case margin. This is the institutional knowledge that normally lives in a wiki nobody reads — encoded, grounded, and queryable.
• History Agent — cross-references prior PRs against the same subsystem, so a change isn't judged in isolation but against the accumulating trend Ajay warned about.
• Estimator Agent — the synthesis step. It takes the diff findings and the budget posture and produces the forecast: +0.4% CPU, +2KB heap, the ISR-latency impact, and where each number lands against its ceiling.
• Report Agent — persists the forecast as a first-class record and shapes the comment that lands on the PR.

Each of these is a StudioX Vibe plus a bot plus a knowledge base — registered, not coded. Add an agent and the reasoning core considers it automatically; change an agent's knowledge and its behavior changes with no release.

How the core reasons, one step at a time

The reasoning core is the project manager. It does not run everything at once and it does not thread raw data between steps. Each round, it looks at the request and the roster, decides which single agent should act next, hands that agent a scoped goal, files the result, and re-plans from what the result actually says.

The order matters, and it's the whole reliability story. The core asks the Budget Agent for the posture first — the ISR deadline, the CPU headroom, the heap ceiling for the touched subsystem. That posture lands in the core's private working notes. Only then does it hand the Diff Analysis Agent a scoped ask: not "tell me everything about this diff," but "estimate the CPU, heap, and interrupt-context cost of these specific changes against this specific budget." A scoped ask comes back small and precise instead of as a raw dump the next step has to wade through. The Estimator Agent then reasons over those two grounded findings — cost and budget — to produce the forecast. Because the core holds the posture, it composes the final number itself rather than trusting any single agent to hold the whole picture.

Observations: you can watch it reason

Everything above streams. As the mission runs, each phase — routing to the Budget Agent, the posture that came back, the scoped diff ask, the estimate — is recorded as a trace event and pushed to the Explain rail over Server-Sent Events, in true execution order. This is not a log you read after a failure. It's the mission narrating itself while it works: selecting Budget Agent… posture retrieved: ISR deadline 250µs, current margin 62µs… estimating diff against posture… +0.4% CPU, +2KB heap, 41µs of the 62µs margin consumed. When an engineer disagrees with a number, they don't file a mystery ticket. They open the trace and see exactly which budget and which finding produced it, and whether the miss was in the code or in the knowledge base — which is where you fix it.

Where the human gates, and where it doesn't

I want to be honest about this, because it's the question every architect asks. The estimating itself is read-only. The Diff Analysis Agent reads the PR; the Budget Agent reads its knowledge base; the Estimator reasons. Nothing about producing the forecast touches your system, and posting the forecast back as a PR comment is a read-only annotation — it informs, it doesn't enforce.

The human-in-the-loop gate exists for the consequential step, and only there. If you configure the mission to take an action with a blast radius — failing a required merge check, or opening a field-escape ticket because a change blew through the ISR deadline — the mission does not just do it. It emits an approval request that lands in the Decision Queue, routes a magic-link approve/reject to the right reviewer, and holds. The action happens on a human "yes." Forecasting is autonomous; enforcement is gated. That boundary is a configuration choice, not a hard-coded one, and I'd encourage teams to start with the estimate as pure advice and add the gate once they trust the numbers.

Instant MCP: wiring your toolchain fast

None of this requires us to hand-code an integration to your source control or your analyzer. StudioX discovers external tools through MCP servers at runtime. Point the mission at your GitHub or GitLab MCP server and your static-analysis MCP server, and the Diff Analysis Agent discovers those tools and starts using them — no rebuild, no redeploy. Swap analyzers next quarter and you register the new server; the agent picks it up on the next run. That's what lets us stand a working estimator up in days against your actual repositories.

And all of it runs inside your own perimeter, next to your source, which is the only place code-level analysis belongs — see Enterprise Deployment for how that's provisioned, and AI Missions for the pattern in general. For what this feels like on a real team's Tuesday, Patrick has the story in In Practice.