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Orchestration

Build, test, and run agentic workflows.

Give workflow owners a visual path from draft to governed release.

Define the steps, choose the workers and tools, add approval points, inspect a dry run, and publish a named version.

Join the waitlistSee the workflow builder

A prompt is not an operating process.

Real work needs context, steps, tools, exceptions, approvals, retries, outputs, and a clear finish. When all of that lives only in code or a chat thread, the people who own the work cannot see it or change it safely.

of agentic AI projects expected to be canceled by end of 2027
>40%
of organizations report agents acting outside their intended scope
80%
Gartner, 2025 · SailPoint, 2025

Example — fictional team and data. Not a live run or customer result.

Workflow builder · Launch Operations

Ship the governed customer import

Rev 14Policy checked
AES-RUN-184Accepted 10:11
  1. Start

    Customer import goal

    Customer import goal brief

    MCMaya Chen
    09:00Completed
  2. Agent

    Draft the spec

    Governed customer import spec

    Aestus Planning
    09:08Completed
  3. Agent

    Write the import

    PR #184 · customer import

    Claude Code
    09:24Completed
  4. Quality check

    Run the checks

    Import validation report

    Aestus Review
    09:41Completed
  5. P-12 · Approval
    Branch

    P-12 · Funds transfer sign-off

    transfer.create · hold for sign-off

    transfer.create · sign-offNo transfer · continue
    10:03Approved
  6. Deliver

    Release the import

    customer-import-v1.0.0 · Accepted

    MCMaya Chen
    10:11Accepted

Turn the process into a visible graph.

  • Define each step

    Choose an agent, branch, quality check, approval, decision, interview, or deliver node.

  • Connect the needed systems

    Add supported tools and repositories, then keep their access tied to the workspace and task.

  • Inspect before release

    Use validation and dry-run evidence to see the planned path, calls, and policy holds before a live run.

Example — fictional team and data. Not a live run or customer result.

Connected systems

Ship the governed customer import

4 connected
  • Claude Code · Coding agentWrote PR #18409:24
  • GitHub · RepositoryPR #184 opened · meridian/customer-import09:24
  • Slack · Approval channelP-12 sign-off request · Priya Raman10:03
  • MCP · MCP clientCard lifecycle · read, claim, update, releaseconnected
Access scoped to Launch Operations · AES-184

Keep release and runtime controls together.

  • Name the decision owner

    Add named approval and choice steps where the work needs a person. Keep the decision and reason with the run.

  • Release a known version

    Review the graph diff, validation state, policy evidence, and candidate hash before promotion.

  • Pin the runtime boundary

    Each run records the workflow release and active policy boundary used to allow, hold, or stop supported actions.

Example — fictional team and data. Not a live run or customer result.

Dry-run report

Ship the governed customer import

rev 14AES-184

  1. 01Goal set

    Would run

    MCMaya Chenwould read the goal brief

    est. 0:20 · 8 credits

  2. 02Spec drafted

    Would run

    Aestus Planningwould write the import spec

    est. 8:00 · 14 credits

  3. 03Code written

    Would run

    Claude Codewould call GitHub · open PR #184

    est. 16:00 · 9 credits

  4. 04Checks run

    Would run

    Aestus Reviewwould call GitHub · run the checks

    est. 17:00 · 7 credits

  5. 05Reviewed & approved

    Would hold

    PRPriya Ramanwould hold for sign-off under P-12

    Every transfer.create call waits for Priya Raman

    waits · 10 credits

  6. 06Shipped & learned

    Would run

    MCMaya Chenwould call Slack · accept customer-import-v1.0.0

    est. 8:00 · 7 credits

6 steps · 1 gate · est. 55 credits· 0 side effects

Start live run

Catch known problems before release.

A graph, access grant, or missing approval can be reviewed before the workflow goes on duty. Runtime controls still matter because a sound configuration cannot prevent every bad input or tool result. Aestus keeps both moments in the same release record.

of breached organizations lacked AI access controls
97%
had no AI governance policy
63%
added breach cost from shadow AI
$670K
IBM, 2025

The numbers this page owns.

Time from idea to a governed, running workflow
Elapsed time from first draft to an approved production version.
Violations caught before launch
Release-blocking findings resolved before a live run.

We already have an agent builder.

Keep it. Aestus connects the agent to shared work, release history, policy, approvals, artifacts, cost, and acceptance.

Build one governed workflow.

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