AI-native control plane

Control agentic workflows before they touch production.

Policy-gated execution, approvals, replay, and audit for agent workflows.

Beecommit adds policy-gated execution, approvals, replay, and audit history to agent workflows.

Discuss a pilot See architecture

Control highlights

Governance has to be visible before the workflow reaches code, infra, or production-facing systems.

Policy gates before side effects

Approvals for high-risk actions

Replayable execution history

Observable from contract to artifact

Fast read

AI pilots are easy to start. Reliable production workflows are not.

The hard part is not generation. It is keeping control once workflows carry state and side effects.

Most teams already know how to make a model generate output. The harder problem starts when agents carry state, trigger side effects, open PRs, call services, or touch production-adjacent systems.

State drifts across long-running tasks

Context fragments, retries change behavior, and teams lose confidence in what the agent actually decided.

Side effects arrive before governance

Code changes, API calls, and environment actions happen faster than policy checks, approvals, and review loops.

Incident review has no durable trail

Teams can inspect outputs, but not always the exact path, checkpoints, and inputs that produced them.

Enterprise controls live outside the workflow

CI, policy, observability, and approval systems exist already. The missing piece is a layer that binds them into agent execution.

How Beecommit works

One governed path from intent to verified artifact.

Beecommit keeps the control loop explicit from contract to replayable result.

Beecommit wraps agent execution in a control loop that stays explicit from the first contract to the final replayable result.

Contract

Define the job as an explicit contract

Capture objective, boundaries, inputs, allowed tools, and expected outcomes before execution begins.

Policy

Evaluate policy gates before risky actions

Apply policy-as-code checks to repositories, environments, budgets, branches, and action classes.

Orchestration

Route work across agents and systems

Coordinate the workflow without losing state continuity across steps, retries, or handoffs.

Execution

Record each decision and side effect

Persist the step history, inputs, outputs, and execution context instead of relying on ephemeral agent memory.

Approval

Pause for human approval where it matters

Bring operators into the loop for high-risk branches, code changes, or production-affecting actions.

Replay

Replay and audit the workflow after the fact

Inspect how a result was produced, which checks passed, and where the workflow branched or converged.

Core capabilities

Control surfaces for real engineering workflows.

The platform exposes the control surfaces needed to run agents inside real engineering constraints.

Beecommit gives platform and engineering teams the primitives needed to run agents inside production-minded operating constraints.

Contract-driven execution

Every workflow starts from an explicit contract instead of a loose prompt and hidden runtime state.

Policy-as-code

Gate repositories, tools, environments, budgets, and action classes with machine-checkable policy.

Human approvals

Insert review checkpoints before merges, deployments, or other high-impact transitions.

Audit-ready history

Keep a durable record of steps, inputs, outputs, checkpoints, and decisions.

Replay and traceability

Reconstruct workflow behavior when teams need incident review, explanation, or evidence packaging.

Safe production workflows

Bound side effects and isolate risky branches instead of letting agents operate as opaque actors.

Observability hooks

Export signals into existing monitoring and operational systems rather than creating a hidden execution island.

Multi-agent orchestration

Coordinate specialized agents without losing governance, continuity, or accountability across the graph.

From chaos to controlled execution

The difference is not model quality. It is control over execution.

The key difference is not model quality. It is whether execution stays bounded and reviewable.

The same agent can feel impressive in a demo and unsafe in production. Beecommit changes the operating model around it.

Uncontrolled execution

Agents can look capable while the workflow remains unsafe.

Prompt starts work without an explicit contract

State shifts across retries and branches

Side effects happen before review

Approvals and audit are reconstructed later, if at all

Beecommit governed flow

The workflow stays bounded, visible, and reviewable from start to finish.

Contract packet

Intent and boundaries are explicit.

Gate

Policy sits before risky transitions.

Approval

Human review appears where it matters.

Verified artifact

Result ships with a replayable path.

Every run begins from a declared contract

Policy gates sit in front of risky transitions

Approvals happen at explicit checkpoints

Replay, audit, and observability stay attached to the workflow

Use cases

Safe automation for engineering work that still needs governance.

Start with repetitive workflows that need review, policy, and a clear artifact trail.

Beecommit fits workflows where the cost of a wrong change is too high for autonomous execution and too high for purely manual operations.

Generate tests under policy

Let agents draft or expand tests while branch, repo, and approval rules stay enforced.

Controlled refactoring

Coordinate multi-step code changes with branch isolation, review checkpoints, and replayable history.

Documentation updates with traceability

Keep docs refreshes tied to an explicit contract, reviewed output, and a retained execution path.

PR-based changes

Route agent output into reviewable pull-request artifacts instead of hidden direct mutations.

Gated production-adjacent tasks

Pause for humans before workflows that affect deployments, policy boundaries, or critical service behavior.

Trust and governance

Built for teams that already think in approvals, policy, and operational evidence.

Beecommit connects approvals, policy, replay, and observability to the runtime instead of leaving them outside the workflow.

Beecommit does not replace existing controls. It connects them to agent execution and makes the path visible.

Approval checkpoints for high-risk workflow branches

Execution history retained as a reviewable timeline

Policy enforcement attached to the workflow, not bolted on after the fact

Replay paths for incident analysis and change review

Role separation between requesters, approvers, and operators

Architecture transparency through docs, APIs, and visible control points

Observability export into existing platform telemetry

Architecture transparency

Enterprise readiness starts with explicit control surfaces

That means clear contracts, bounded side effects, transparent approvals, and a durable execution record that engineering leaders can actually review.

See architecture Read the docs

Next step

Start with one governed workflow and evaluate it like infrastructure.

The fastest path is a scoped pilot: pick a high-value workflow, define the contract, connect the gates, and review the execution trail with your platform team.

Architecture review first. Pilot second. Expansion only after the control loop is proven.

Discuss a pilot Read the docs

See product tour•GitHub repository•Architecture review docs