AES-179
PR
Transfer customer settlement funds
Held by policy · P-12 · $80,000
Waiting on Priya Raman · 10:03
Help your team get more from AI.
Bring people and AI agents into one workspace. Connect goals, workflows, and reviews to see where AI can help your team.
AI use is growing. What is it helping you achieve?
More agent activity does not tell you whether work is useful. Teams need to see which results they can use, what those results cost, and which goals they serve.
- growth in weekly ChatGPT Enterprise messages over a year
- 8×
- growth in average OpenAI reasoning-token use per organization over a year
- 320×
- of ChatGPT Enterprise messages processed through a Custom GPT or Project in recent months
- 20%
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
Useful results need more than output.
Teams can produce more with AI and still struggle to see business value. In McKinsey’s 2025 survey, 62% were at least experimenting with agents, while 39% reported enterprise-level EBIT impact from AI. EBIT is profit before interest and tax. These are separate survey measures, not proof of cause. Four common gaps make value harder to see.
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.
Spend without a clear result
A model bill shows usage. It does not show the review, retries, and rework needed to finish a useful task.
Unclear limits and approvals
When an agent can act on company systems, teams need to know its limits, who must approve, and what happened.
- of organizations described as seeing no return in Project NANDA’s preliminary report
- 95%
- of survey respondents meet McKinsey’s definition of an AI high performer
- 6%
- report that AI operating costs, including tokens, constrained their use
- 1 in 5
Plan, work, learn, improve, and stay in control.
Connect the goal to the people and agents doing the work. Keep context, costs, and decisions close enough to help with the next step.
- 01Plan toward a goalLink AI effort to the priorities it is meant to serve.
- 02Work togetherGive people and agents a shared place to build and finish work.
- 03Keep useful contextLet the next task benefit from past decisions and their sources.
- 04Improve valueCompare the cost and quality of completed work.
- 05Keep controlSet limits and keep approvals with the work.
1 · Strategy & Goals
Connect AI effort to the goals that matter.
Bring objectives, projects, and tasks together so people and agents can see why the work matters. Follow progress and recorded AI spend as the work moves.
Status from the work itself
Task updates help keep goal progress current, with less status chasing. Review blocked work and decide who can help.
See progress and spend together
Explore the work behind a goal and its recorded costs. Use that view to discuss priorities and where to put more effort.
Explore the work behind a goal.
Select a goal or project. Review its progress, spend, and next step.
Select a card to open its details.
All goals, people, amounts, and status values here are fictional. Recorded spend depends on the connections in use. Review coverage before comparing teams. Try the example. Changes stay in this demo.
2 · Workspace & Orchestration
Give people and agents one place to work together.
Keep assignments, conversations, documents, and reviews on the work itself. A shared view can help your team spend less time passing files and rebuilding context.
Clear owners, shared context
People and agents take assignments on the same board. The brief, decisions, and latest work stay available for the next handoff.
Build workflows visually
Arrange the steps, choose people and agents, and add reviews. Preview the planned work before a live run.
Keep the tools your team uses
Connect supported agents and tools such as Claude Code, Codex, GitHub, and Slack. Check each integration for its scope and setup needs.
Example — fictional team and data. Not a live run or customer result.
Launch Operations · AES / Board
10:11
Featured · Sandboxes early access
Run coding agents with limits your team sets.
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.
Define the session’s access
Set the repositories, network access, and credentials available to the session. Review blocked requests alongside the work.
Keep the session record
Review the terminal record, tools, policy refusals, and cost with the work.
Example — fictional team and data. Not a live run or customer result.
Example terminal session
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 · AI Cost Optimization
Find room to lower the cost of useful work.
Look beyond model spend to retries, review, and rework. Compare completed workflows to find changes that may lower cost while meeting your quality requirements.
Learn from completed work
Use past decisions and results to guide the next task. Look for repeated mistakes and work your team should not need to repeat.
Review changes before you adopt them
Compare the cost and quality of a proposed change. Keep the decision with your team and the limits you set.
Ship the governed customer import
AcceptedCompletion cost by accepted run
Indexed cost per accepted run · run 1 = 100
0255075100
−45% vs Run 1
Run
Optimization events
- Run 3 · −8Less preparation in this example
- Run 6 · −9Lower model spend in this example
- Run 8 · −6Less rework in this example
Changes in the illustrative cost breakdown
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.
Completion cost values: Run 1: 100; Run 2: 94; Run 3: 86; Run 4: 82; Run 5: 78; Run 6: 69; Run 7: 66; Run 8: 60; Run 9: 58; Run 10: 55. Optimization events: Run 3: Less preparation in this example; Run 6: Lower model spend in this example; Run 8: Less rework in this example. Illustrative reduction by component: Model 30 → 15; Context preparation 18 → 7; Retries 12 → 4; Rework 8 → 4; Coordination 10 → 7; Tools 10 → 8; Review time 12 → 10.
4 · Security & Oversight
Know what agents may do, and review what they did.
Set access limits and approval requirements for supported agent work. Keep a record of actions and decisions to help your team review them later.
Before release
Review access and approval needs before a workflow starts. Finding a problem here may prevent costly rework later.
During the run
Supported actions can continue, wait for approval, or stop according to the controls you set.
After the decision
See who acted, what was approved, and the result. Give reviewers context for their own assessment.
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
Your tools, connected around the work.
Aestus brings goals, teamwork, workflows, cost, and oversight into one view. Start with a supported connection and see whether it helps your team finish useful work.
Start with a workflow that matters to you.
Join the waitlist for early access. When access is available, choose a workflow with a clear result and compare its cost, time, and quality before expanding.
Talk to us