Infrastructure for governed AI agents.
We are the agent harness for production AI — the entire runtime your agents operate inside: the loop, tools, guardrails, evaluation, and live observability, on an immutable system of record. Not a layer bolted on. The whole harness.
Core platform primitives.
Six building blocks that turn experimental agents into governed production systems.
Policy Engine
Codify permissions, approval rules, escalation paths, and safety limits for agent actions. Define what agents can do, and when human sign-off is required.Agent Observability
Capture traces across prompts, tools, memory, retrieval, APIs, model calls, latency, cost, and outcomes. Full visibility into every agent run.Evaluation Harness
Run scenario tests, regression suites, red-team checks, and quality gates before and after deployment. Measure before you ship.System of Record
Not a log — a durable, queryable record of every agent action, decision, approval, failure, and override. Attribution, replay, and audit come built in.Human-in-the-Loop Controls
Route sensitive actions to human review without breaking the agent workflow. Keep oversight intact while maintaining operational speed.Forward-Deployed Engineering
Our engineers integrate with your systems, data, and APIs — solving the problem at the core of your infrastructure, not at the edges. From pilot to production.How the agent harness works.
An agent reasons, acts on the market, and responds to each observation — the agentic loop. We are the harness around it: every action clears a policy gate, observability traces it live, and an immutable system of record sits beneath as the foundation. Fully autonomous workflows stay inside sandboxed limits; human-in-the-loop workflows hold high-stakes actions for review.
Diagram: an AI agent calls out to LLMs, specialized models, MCP tools, market data, user data, and sub-agents — each call traced — then proposes an action; a policy gate checks it against limits and permissions. Approved actions reach the market; rejected actions return to the agent to re-plan. In human-in-the-loop mode, held actions wait for a human decision before executing. The market returns an observation and the outcomes of the action to the agent, closing the loop. Every step is traced, and everything is written to an immutable system of record.
Autonomous systems need operating constraints.
They need test coverage, monitoring, and clear accountability. Reinforce Market Labs provides the control plane that lets teams safely move agents into workflows where mistakes are expensive.
From prototype to production, the gap is not model capability. It is orchestration, policy enforcement, evaluation discipline, and operational accountability. That is what we build.
Bring production discipline to your agent stack.
Talk to us about your current agent architecture. We will help identify where policy, observability, and evaluation fit in.