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AI agents are only as good as the workflows behind them.

Connect people, systems, and AI agents into governed workflows that produce reliable, auditable business operations.

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A robust governed AI workflow, released and evidenced.A released, governed workflow frame. Work arrives, an AI agent runs the routine path under scoped permissions, and a policy gate routes the result: the routine path commits directly, while exceptions and high-risk steps go to a human owner who approves and rejoins the same commit, and a failed check re-runs the agent. Every step lands in a hash-chained, replayable record.GOVERNED AI WORKFLOWGOVERNEDWORKFLOW · V1.4RELEASED · 2026-07-24RE-RUNEXCEPTIONAPPROVEDPASSPOLICY GATEINPUTtriggerWork arrivesAI AGENTscopedRuns the routineCOMMITverifiedCommit resultOUTPUTYour systemsAPPROVALowner decidesPerson approvesA robust governed AI workflow, released and evidenced.GOVERNED AI WORKFLOWGOVERNEDWORKFLOW · V1.4RELEASED · 2026-07-24PASSEXCEPTIONAPPROVEDGATEINPUTtriggerWork arrivesAI AGENTscopedRuns the routineCOMMITverifiedCommit resultOUTPUTYour systemsAPPROVALownerPerson approves
Run recordHash-chained · replayable
#NodeEventRecord
01Inputrequest receiveda7f3
02AI agentagent draftedb1c9
03Policy gatepolicy checked4e08
04Approvalowner approvedd2b6
05Commitresult committed9c47
06Outputwritten back1f5e
Who it's for

Built for the people accountable for AI agents in production.

Governed AI workflows bring three roles together: the engineers who put agents into production, the operators who own the process, and the risk teams who have to account for what the agent did.

A platform engineer working at a multi-monitor code workstation
01 / Build

Platform and engineering leaders

You are asked to put AI agents into real operations. Kora gives you one place to run them under a released process version with scoped permissions, so a change to agent behavior ships through the same release path as any other change.

Release control. Agent changes ship like any other change.
An operations manager reviewing a live operations display
02 / Run

Operations and process owners

You own the process, not the model. Approval points stay where judgment is needed, agents take the routine path, and you move more work to them as trust grows, without rebuilding the workflow each time.

You own the decisions. Approvals stay where judgment is needed.
A compliance reviewer examining a printed audit record
03 / Prove

Risk, compliance, and audit

You have to answer what the agent did, when, and why. Every action is observable at runtime and leaves a hash-chained, replayable record, so an approval or an exception traces straight back to its evidence.

Evidence by default. Every action, hash-chained and replayable.
§ 01 · The approach

Governed AI Workflows

Kora runs AI agents inside governed workflows: each agent acts under a released process version, with scoped permissions, human approval points, full observability, and a hash-chained, replayable record of every action it takes.

01Release

Agents under release control

Agents execute the released version of the process, not an improvised script. Changes to agent behavior ship through the same release path as any other process change.

  • Released version, not a script
  • Scoped permissions
  • Same path as any change
02Approve

Humans in the loop by design

Approval points are part of the workflow definition. Agents handle the routine path and route exceptions to the person who owns the decision.

  • Approvals in the definition
  • Agents run the routine path
  • Exceptions go to the owner
03Prove

Every agent action audited

Each agent step is observable at runtime and leaves a hash-chained, replayable record, so you can always answer what the AI did, when, and why.

  • Observable at runtime
  • Hash-chained, replayable
  • What, when, and why
§ 02 · Related use cases

See it in practice

Concrete workflows teams put into production for this outcome.

§ 03 · FAQ

Questions, answered.

What teams ask when they evaluate Kora for this problem.

Only inside guardrails. In Kora, agents act under a released process version with scoped permissions and defined human approval points, and every action leaves a hash-chained, replayable record. Trust comes from the governance around the agent, not from the model alone.

Agents run as workflow participants: the released process defines what they may do, working memory scopes what they see, humans approve the steps that need judgment, and runtime observability plus a hash-chained, replayable record capture what happened.

The workflow routes the case to a human owner with full context. Exceptions are a designed path in the process, not a failure mode.

A governed AI workflow is a business process that runs AI agents under explicit controls: a released process version defines what each agent may do, scoped permissions bound what it sees and touches, human approval points cover the steps that need judgment, and a hash-chained audit trail records every action for replay.

Exception-heavy operations where AI acts and someone is accountable for the result: invoice processing, AML and KYC reviews, quality inspections, procurement exceptions. Agents handle the routine path, people approve what needs judgment, and the same audit trail covers both.

Yes, when control lives in the process rather than in the model. A released workflow version fixes the path, so runs are repeatable; scoped permissions bound what an agent can see and touch; human-in-the-loop approval points cover the decisions that carry risk; and every action lands in a hash-chained, replayable record. That combination is what makes AI viable in operations where an error is expensive: finance, quality, compliance, and safety-adjacent work.

Frameworks help developers build agents, and the agent is the unit of control. In Kora the workflow is the unit of control: agents participate in a released business process alongside people and systems, with approvals, scoped permissions, observability, and a hash-chained record built into the run. Teams choose Kora when the process matters enough to govern, not just to automate.
§ 04 · Next step

Bring one agent you want in production.

In a demo, bring a process an agent should run. We will put it inside a governed workflow: a released version, scoped permissions, approval points where judgment is needed, and a hash-chained record of every action it takes.

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