Turn AI agent operations into accepted results.

Aestus is one record for work done by people and AI agents: why it started, who did it, what it produced, who approved it, what it cost, and whether the result was accepted.

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More AI work is easy. More accepted work is not.

AI use is rising fast. The harder job is turning that activity into work the business can accept, measure, and trust.

growth in enterprise AI message volume in a year: 8×
growth in reasoning-token consumption per organization: 320×
enterprise AI runs through recurring workflows, not one-off prompts: 1 in 5

OpenAI, State of Enterprise AI 2025

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

Ship the governed customer import

AES-RUN-184 · Launch Operations

09:00MCGoal setMaya Chen · Customer operations leadCustomer import goal brief
09:08Spec draftedAestus Planning · Planning agentGoverned customer import spec
09:24Code writtenClaude Code · Coding agentPR #184 · customer import
09:41Checks runAestus Review · Review agentImport validation report
10:03PRReviewed & approvedPriya Raman · Security approverP-12 approval record
10:11MCShipped & learnedMaya Chen · Customer operations leadcustomer-import-v1.0.0Accepted

Why was this result sent back?

Choose a fictional research brief or code handoff. Read the draft, check the rules, and follow the correction to a review decision.

Fictional review examples. All notes, people, outputs, and decisions are made up. No customer data or live action is used.

Read both full transcripts

Full review transcripts

Fictional review examples. All notes, people, outputs, and decisions are made up. No customer data or live action is used.

Research brief

Three fictional interview notes. These people are not Aestus customers.

Brief: Summarize the three fictional interview notes. Link each claim to a quote. Keep names private.

Cover all three notes and attach a matching quote to each summary claim.
Use only anonymous note labels; include no person or company names in the output.

Source material:

Note A: “I need to see the source beside the claim.”
Note B: “I cannot tell which draft is ready to use.”
Note C: “Tell me the checks before the work starts.”

1. Set the brief and two rules

Research lead (fictional) · Brief v1

The three anonymous source notes below are the complete input. The reviewer will check both rules before using the summary.

2. Read the first draft

Research agent (fictional) · Summary v1

Note A: The reviewer needs the source beside each claim. Quote A: “I need to see the source beside the claim.”

Note B: The handoff needs a clear version. Quote B: “I cannot tell which draft is ready to use.”

Note C: The reviewer needs the checks before work starts. Quote: missing.

3. Check the draft against both rules

Research reviewer (fictional) · Summary v1

All three notes are summarized, but the third claim has no quote. A plausible summary is not enough.

Rule 1: Not met: the Note C claim has no matching quote attached.
Rule 2: Met: the output uses only anonymous note labels.

4. Send the draft back

Research reviewer (fictional) · Summary v1 — sent back

Decision: send back Summary v1. Add the exact Note C quote to its claim. Keep the anonymous labels. This version is not accepted.

5. Inspect the correction

Research agent (fictional) · Summary v2

Note A: The reviewer needs the source beside each claim. Quote A: “I need to see the source beside the claim.”

Note B: The handoff needs a clear version. Quote B: “I cannot tell which draft is ready to use.”

Note C: The reviewer needs the checks before work starts. Quote C: “Tell me the checks before the work starts.”

6. Record the review decision

Research reviewer (fictional) · Summary v2 — accepted

Decision: accept Summary v2 against these two rules. The reviewer checked all three claims against their supplied notes. Summary v1 remains sent back; the decision applies to v2 only.

This fictional decision shows the review method. It does not prove that AI output is always correct.

Rule 1: Met: all three notes have a summary claim and a matching quote.
Rule 2: Met: the output contains no person or company names.

Code release handoff

A separate fictional review of the Meridian customer-import release handoff. This is not the original 71-minute journey or its P-12 approval story.

Brief: Prepare the customer-import-v1.0.0 release handoff for Maya. Show the required checks and explain how to return to the prior release.

Include results for invalid-row rejection and duplicate-safe retry checks.
Include rollback instructions and name the person who owns the rollback decision.

Source material:

Fictional check report: invalid-row rejection passed; duplicate-safe retry passed.
Fictional release owner: Maya. No repository, deploy, or external tool is connected to this example.

1. Set the brief and two rules

Maya, release owner (fictional) · Brief v1

A release handoff needs more than a passed check. Maya will compare the written handoff with both rules before accepting it.

2. Read the first draft

Coding agent (fictional) · Release handoff v1

Release handoff v1: customer-import-v1.0.0. Check report: an invalid row is rejected; retrying a valid row does not create a duplicate. Both checks pass in this fictional report.

Rollback instructions: missing.

3. Check the draft against both rules

Maya, release reviewer (fictional) · Release handoff v1

The required check results are present. The handoff does not say how to return to the prior release.

Rule 1: Met: invalid-row rejection and duplicate-safe retry results are included.
Rule 2: Not met: rollback instructions and their decision owner are missing.

4. Send the draft back

Maya, release reviewer (fictional) · Release handoff v1 — sent back

Decision: send back Release handoff v1. Add rollback steps and the decision owner. Passed checks do not replace the missing handoff requirement.

5. Inspect the correction

Coding agent (fictional) · Release handoff v2

Release handoff v2: customer-import-v1.0.0. Check report: an invalid row is rejected; retrying a valid row does not create a duplicate. Both checks pass in this fictional report.

Rollback instructions: pause new imports, restore the prior release, then check the saved row count before imports resume. Maya owns the rollback decision. These are fictional handoff notes, not instructions for a real system.

6. Record the review decision

Maya, release reviewer (fictional) · Release handoff v2 — accepted

Decision: accept Release handoff v2 against these two rules. The reviewer checked the report and rollback notes. Release handoff v1 remains sent back; the decision applies to v2 only.

Accepting this fictional document does not deploy code, authorize a payment, or prove production safety.

Rule 1: Met: both required check results remain in the handoff.
Rule 2: Met: rollback steps are present and Maya owns the decision.

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Output is not the result.

62% of organizations are testing agents, but only 39% report enterprise-level EBIT impact. Most of that group attributes less than 5% of EBIT to AI (McKinsey, 2025). Four missing links keep activity from becoming accepted work.

Work without a goal
Output grows, but nobody can show which goal it served or what should stop when priorities change.
Handoffs without one record
Briefs, runs, decisions, and reviews sit in separate tools. Each person rebuilds the story.
Cost without acceptance
A model bill says what a call cost. It does not say whether the final result was accepted.
Action without authority
80% report unintended agent actions (SailPoint, 2025). A log alone cannot show whether an action was allowed.

One record from goal to accepted result.

Aestus joins the plan, the work, the run, the decision, and the outcome. Start with one workflow, not a migration.

1 · Strategize & Plan

Point your AI spend at what matters.

Connect goals to the projects, tasks, runs, and artifacts that serve them. The reason for the work stays attached as it moves.

Status from the work itself
People and connected agents report progress on the task, so the goal view stays tied to live work.
Trace work back to purpose
Move from a goal to the work, runs, and artifacts beneath it without rebuilding the chain.

The number this section owns: share of AI work tied to a goal.

Connect a goal to one workflow

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

Launch Operations · Goal tree09:08

GoalLaunch the governed customer importOn trackMCMaya Chen · 09:00
17%
ProjectCustomer data launch1 taskActive
AES-184Ship the governed customer importIn progressAestus Planning · drafting the spec09:08

2 · Execute

Give every handoff one home.

Keep the brief, assigned worker, discussion, artifact, review, and acceptance decision on the same card.

A clear owner at each step
Assign work to a person, agent, tool, or workflow. Keep each handoff and decision with the task.
Build the path visually
Lay out steps, choose participants, set approval points, and inspect a dry run before release.
Keep the tools your team uses
Connect Claude Code, Codex, Cursor, GitHub, Slack, or any supported MCP client to the same task lifecycle.

The numbers this section owns: first-pass acceptance rate · request-to-completion cycle time.

Watch a workflow reach completion

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

Launch Operations · AES / Board

10:11

Ready0

The goal is in motion

In progress0

No other active execution

Approval1

AES-179

Transfer customer settlement funds

Held by policy · P-12 · $80,000

Waiting on Priya Raman · 10:03

Complete1

AES-184

Ship the governed customer import

Accepted

customer-import-v1.0.0 · 10:11

Featured · Sandboxes early access

Keep the coding agent. Move the risk off the laptop.

Early-access teams run Claude Code and Codex in remote sandboxes with organization-set access to repositories, networks, credentials, tools, models, and budgets.

Use the same command
Start Claude Code or Codex from your terminal. The session runs in the governed sandbox.
Set one enforced boundary
Name what the session can reach. Other network and credential requests are refused and recorded.
Keep the session record
Review the terminal record, tools, policy refusals, and cost with the work.

See Aestus Sandboxes

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

Developer terminal

Live

claude
sandbox ready · policy "Engineering default" v7 · egress restricted
Claude CodeReading src/billing/invoice.ts
Claude CodeEdit src/billing/invoice.ts
Blocked by policy: api.unknown-host.dev
Claude CodeRan tests · 41 passed

3 · Accelerate Results

Know what accepted work costs.

Aestus records model and tool spend, time, retries, review, rework, and acceptance for the complete workflow.

Compare complete runs
See which workflow and producer reached acceptance, what it cost, and where time or retries accumulated.
Test changes before release
Replay, evaluate, and canary a candidate. Promote it only after the evidence meets your quality bar.

The number this section owns: cost per completed workflow.

Measure one workflow's baseline

Ship the governed customer import

Accepted

Completion cost by accepted run

Indexed cost per accepted run · run 1 = 100

0255075100

−45% vs Run 1

Run

Optimization events

Run 3 · −8Context reused
Run 6 · −9Smaller model · acceptance unchanged
Run 8 · −6Policy check moved pre-execution

Where the cost came down

Run 1Run 10

Model: 30 → −15
Context preparation: 18 → −11
Retries: 12 → −8
Rework: 8 → −4
Coordination: 10 → −3
Tools: 10 → −2
Review time: 12 → −2
Total: 100 → 55−45

Fictional example — the 100-to-55 curve is not measured savings or a customer result. Measure your own workflow to establish a baseline.

4 · Secure Everything

Decide before the action. Prove it after.

Aestus keeps the active policy, approval decision, actor, action, and evidence with the run that produced the result.

Before release
Review the workflow definition, tool access, and approval path before a new version goes on duty.
During the run
A governed action can continue, wait for a named approver, or stop under the pinned policy boundary.
After the decision
Keep the actor, authority, policy, action, and hash-chained receipt in one verifiable record.

The number this section owns: time-to-proof — elapsed time to show what an agent did and which policy applied.

Get a governance review of one live workflow See governed sandboxes

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

Dry-run report

Ship the governed customer import

rev 14AES-184

01Goal set
Would run
MCMaya Chenwould read the goal brief
est. 0:20 · 8 credits
02Spec drafted
Would run
Aestus Planningwould write the import spec
est. 8:00 · 14 credits
03Code written
Would run
Claude Codewould call GitHub · open PR #184
est. 16:00 · 9 credits
04Checks run
Would run
Aestus Reviewwould call GitHub · run the checks
est. 17:00 · 7 credits
05Reviewed & approved
Would hold
PRPriya Ramanwould hold for sign-off under P-12
Every transfer.create call waits for Priya Raman
waits · 10 credits
06Shipped & learned
Would run
MCMaya Chenwould call Slack · accept customer-import-v1.0.0
est. 8:00 · 7 credits

Trackers hold tasks. Builders make agents. Gateways price calls. Suites govern identity.

Aestus joins those pieces around one accepted result. It connects to the tools you already run.

Start with one workflow.

Use one real workflow to establish the cost, time, quality, and approval baseline. Then decide what is worth improving.

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Turn AI agent operations into accepted results.

More AI work is easy. More accepted work is not.

Why was this result sent back?

Full review transcripts

Research brief

1. Set the brief and two rules

2. Read the first draft

3. Check the draft against both rules

4. Send the draft back

5. Inspect the correction

6. Record the review decision

Code release handoff

1. Set the brief and two rules

2. Read the first draft

3. Check the draft against both rules

4. Send the draft back

5. Inspect the correction

6. Record the review decision

Output is not the result.

Work without a goal

Handoffs without one record

Cost without acceptance

Action without authority

One record from goal to accepted result.

Point your AI spend at what matters.

Status from the work itself

Trace work back to purpose

Give every handoff one home.

A clear owner at each step

Build the path visually

Keep the tools your team uses

Transfer customer settlement funds

Ship the governed customer import

Keep the coding agent. Move the risk off the laptop.

Use the same command

Set one enforced boundary

Keep the session record

Know what accepted work costs.

Compare complete runs

Test changes before release

Completion cost by accepted run

Decide before the action. Prove it after.

Before release

During the run

After the decision

Trackers hold tasks. Builders make agents. Gateways price calls. Suites govern identity.

Start with one workflow.