AI agent control

Autonomous coding work needs a budget, a boundary, and proof.

Runcap is the public product. Consulting extends the same control model into team policy, workflow integration, evidence design, and cost analysis.

EstimateModel a routed run as a range before execution.
CapEnforce a configured ceiling before the next routed request is sent.
ConstrainDeclare allowed scope and escalate policy or verifier changes.
ReplayVerify permitted changes from base-commit evidence before merge eligibility.

What Runcap does today

Runcap is a free, MIT-licensed, local-first command-line tool. It provides pre-run estimation and a configured cap for requests routed through its local gateway. Its GitHub Proof Gate can replay permitted changes against base-commit policy and verification, returning PASS, BLOCKED, or HUMAN_APPROVAL_REQUIRED.

What it does not claim

It does not see every cost in a flat-rate subscription, guarantee a task outcome, replace CI, or prove that arbitrary code is correct. A green check is evidence only for the checks that ran under the policy that protected them.

Where consulting begins

Teams may need model and provider usage normalized, task categories defined, approval tiers, protected verification assets, budget policy, evidence retention, dashboards, and a safe rollout around existing tools. That design is specific to the team and cannot be honestly packaged as a universal switch.

Why this is distinct from observability

Observability explains what happened. Control changes what is allowed to happen next. Runcap focuses on enforceable boundaries and merge evidence; it can complement tracing and routing tools rather than pretending to replace them.

Why a green CI check is not enough

The agent may be able to change the checks that judge its work.

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The retry loop is a cost problem

Agent activity can look productive while repeating without useful progress.

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Compare control layers

Where Runcap, Langfuse, Helicone, LiteLLM, and routing tools have different jobs.

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