Governed AI Execution
Deterministic execution for enterprise AI workflows.
Why a harness matters
AI pilots often work because the scope is narrow, the context is curated, and the risks are contained.
Production is different.
Enterprise workflows need to run across real systems, use approved context, respect access rules, recover from failures, and show what happened after the fact. When state lives inside a session, governance lives inside prompts, and debugging depends on chat history, teams struggle to make AI work repeatably.
The Agent Harness gives each workflow a governed execution spine, so AI-enabled work can be run, inspected, replayed, and improved over time.
What the Agent Harness does
Runs every workflow through a governed loop
Each turn follows a defined sequence: assemble the prompt, manage context, invoke the model, validate the result, execute approved tools, collect outputs, evaluate controls, and decide whether to continue, branch, or stop.
Keeps execution deterministic
The Harness runs the loop, not the model. Stages execute in a fixed order, and evaluations gate transitions before actions leave the runtime.
Captures replay-ready trajectories
Step logs and working-state snapshots are written at the turn boundary, making sessions easier to replay, debug, recover, and evaluate.
Recovers without starting over
If a session crashes, idles, or moves during a rolling deploy, the Session Router can direct a compatible Harness to resume from the last committed turn.
Makes observability standard
The Agent Harness emits standard operational signals, including traces, metrics, structured logs, and live event streams, so teams can observe AI workflow execution with the tools they already use.
Built for real enterprise workflows
The Agent Harness is stateless and event-driven by design. Agent state is externalized rather than trapped in a long-running process, which makes workflows easier to resume, replay, scale, and operate.
A profile manifest binds the workflow’s prompt, permissions, model policy, seeded context, and execution rules. That gives each workflow a clear operating envelope while allowing many sessions to run through the same Harness profile.
The result is a runtime that supports high-concurrency and scalable AI work without giving up control, auditability, or recovery.
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