Orchestration
Make the workflow clear.
Bring people, agents, and approvals into one visible process.
Arrange steps visually, connect supported tools, and preview the plan before you run it.
An agent needs a process around it.
Once an agent is built, the team still needs to decide what it should do, which information it needs, and when a person should step in. A shared workflow makes those choices easier to discuss and review.
- of agentic AI projects expected to be canceled by end of 2027
- >40%
- of organizations report agents acting outside their intended scope
- 80%
Inspect the workflow before it runs.
Select a step to see what it can read or change. Fix the broad grant before the draft starts.
Illustrative example
Draft the update
Write a customer update from the brief. The model cannot approve or send it.
- Estimated step cost
- $2.40
- Repository access
- All repositories
The tool grant covers more repositories than this task needs.
Draft customer update
- Current access
- All repositories
- Required access
- Refund repository
Try the example. Changes stay in this demo.
Make the workflow clear before it starts.
Design and adjust the steps visually
Choose the work, its participants, and the reviews it needs. Workflow owners can see the process and change supported steps without writing code.
Connect the needed systems
Use supported agents, models, repositories, and business tools. Review each connection’s access and setup needs before adding it to the workflow.
Preview the planned work
Use a dry run to inspect the expected steps and approval needs. A preview can reveal gaps, but live results still need review.
Example — fictional team and data. Not a live run or customer result.
Example connections
Ship the governed customer import
- 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
Match approvals to the way your team works.
Name the decision owner
Choose who reviews sensitive steps and outputs. Keep their decision and reason with the work, so the next participant can see what was agreed.
Review changes before release
Check the proposed changes, access needs, and review results before putting a new version into use.
Keep controls in place during the work
Supported actions can proceed, wait for approval, or stop under your rules. Coverage depends on the tools and execution mode in use.
Example — fictional team and data. Not a live run or customer result.
Example dry-run report
Ship the governed customer import
rev 14AES-184
01Goal set
Would runMCMaya Chenwould read the goal brief
est. 0:20 · 8 credits
02Spec drafted
Would runAestus Planningwould write the import spec
est. 8:00 · 14 credits
03Code written
Would runClaude Codewould call GitHub · open PR #184
est. 16:00 · 9 credits
04Checks run
Would runAestus Reviewwould call GitHub · run the checks
est. 17:00 · 7 credits
05Reviewed & approved
Would holdPRPriya Ramanwould hold for sign-off under P-12
Every transfer.create call waits for Priya Raman
waits · 10 credits
06Shipped & learned
Would runMCMaya Chenwould call Slack · accept customer-import-v1.0.0
est. 8:00 · 7 credits
Look for problems while they are easier to change.
Review access, spending limits, and approval needs before release. This may help your team catch problems earlier. Checks during live work still matter; a preview cannot rule out every risk.
- of organizations with AI-related breaches lacked proper AI access controls
- 97%
- of surveyed organizations lacked AI governance policies
- 63%
- higher average breach cost with high shadow AI use versus low or no use
- $670K
Measure the path from idea to useful work.
- 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.
An existing builder can remain part of your setup. Aestus gives supported agents a shared workspace with goals, human reviews, costs, and results to help your team manage the wider process.