Autonomous agent operations coordination portal

For

Executives and strategy teams running recurring business work through AI agents

Solves

Agent work is scattered across rented tools, so goals, approvals and results are hard to coordinate or audit.

Delivers

Reviewed agent operations portal with persistent memory and a live record of agent actions

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Run business work autonomously with AI agents that plan, execute and collaborate on tasks.

1. Create agents from a single prompt or imported code.
2. Retain context and state across interactions.
3. Assign roles such as COO, CMO or CTO and delegate tasks.
4. Interact via chat, voice calls or direct task assignment.
5. Run agents continuously without manual triggering.
6. Give each agent its own email, phone, browser, Slack and wallet.
7. Let multiple agents work in shared collaboration spaces.
8. Import public code repository links to create working agents.
9. Approve, override or incrementally trust agent actions.
10. Report progress and outcomes on a live dashboard.
11. Let an AI CEO accept goals or KPIs, plan and assign tasks.
12. Provide ready-to-use role agents without coding or API keys.
13. Convert vague objectives into measurable signals and task threads.
14. Connect agents to Slack, GitHub, Stripe, GA4 and PostHog.
15. Keep content and decisions aligned with past work and preferences.
16. Manage different AI models and integrations.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed agent operations portal with source references and unresolved questions.

Everything these tools do, in one app

• Autonomous agent creation Create AI agents from a single prompt or by importing code, without manual setup.Found in Verse, SureThing.io, Tycoon AI
• Persistent memory Agents retain context and state across interactions, resuming tasks instead of starting fresh.Found in Verse, SureThing.io, Tycoon AI
• Role-based agent teams Agents take on roles like COO, CMO, or CTO and collaborate by sharing context and delegating tasks.Found in SureThing.io, Tycoon AI
• Multi-channel interaction Interact with agents via chat, voice calls, or direct task assignment.Found in Verse
• 24/7 independent operation Agents run continuously without requiring a human to trigger or supervise every action.Found in Verse
• Dedicated agent infrastructure Each agent has its own email, phone, browser, Slack, and crypto wallet, reducing manual integrations.Found in Verse
• Shared collaboration spaces Multiple agents work together in shared spaces to delegate tasks and solve complex problems.Found in Verse
• One-click code import Import public code repository links to create working agents from existing open-source skills.Found in SureThing.io
• Human-in-the-loop controls Approve, override, or incrementally build trust in agent actions.Found in SureThing.io
• Live dashboard and reporting Agents report progress and outcomes in human-friendly formats via a live dashboard.Found in SureThing.io
• AI CEO orchestration An AI CEO accepts goals or KPIs, breaks them into plans, assigns tasks, and requests approvals.Found in Tycoon AI
• Ready-to-use agents Pre-built agents for roles like CMO, CTO, and researchers are available without coding or API keys.Found in Tycoon AI
• Goal-to-workflow conversion Translate vague objectives into measurable signals, timeframes, and actionable task threads.Found in Tycoon AI
• Tool integrations Agents interact with existing stacks like Slack, GitHub, Stripe, GA4, and PostHog.Found in Tycoon AI
• Brand alignment Memory-driven context helps content and decisions reflect past work and preferences.Found in Tycoon AI
• Model management Supports managing different AI models, such as Claude Code or Hermes integrations.Found in Tycoon AI

What goes in, what comes out

The workflow

1. In
   Start with

   Goals, KPIs, role definitions, tool access and approval rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect goals

4. 3

   KPIs

5. 4

   Role definitions

6. 5

   Tool access and approval rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed agent operations portal with persistent memory and a live record of agent actions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Agent autonomy is bounded by named-owner approval, spending limits and tool permissions; final business decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Goal and KPI intake, Agent team and role setup, Live operations dashboard, Approval and override queue, Shared collaboration space, Reporting and audit log. Use a portfolio view for goals, a central board for running agent tasks, and a right-hand panel for roles, memory, tool access and comments. Let users compare agent plans side by side. Display draft, running, awaiting approval, blocked and completed states. Provide a client preview link with comments anchored to the relevant task. Make the task-specific outcome reviewed agent operations portal with persistent memory and a live record of agent actions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, memory records, client comments, approval states, tool permissions, spending limits, action history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Client-owned goal documents, authorized KPI exports and permitted tool accounts. Slack, GitHub, Stripe, GA4, PostHog, email, calendar and cloud storage. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: create agents from a single prompt or imported code; retain context and state across interactions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will executives and strategy teams running recurring business work through AI agents use it to solve "agent work is scattered across rented tools, so goals, approvals and results are hard to coordinate or audit"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Approved agent tasks per operating week and rework after human review.
4. Measure, then decide. Track approved agent tasks per operating week and rework after human review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One bounded goal set and one agent team; final business decisions and external actions remain human-approved. Implement one approved input format, a bounded representative case set and the first two task modules: create agents from a single prompt or imported code; retain context and state across interactions. Support the third module with operator review: assign roles such as COO, CMO or CTO and delegate tasks. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed agent operations portal with persistent memory and a live record of agent actions. Retain the explicit scope boundary: One bounded goal set and one agent team; final business decisions and external actions remain human-approved.

What the build depends on. Agent runtime, persistent memory store, tool credentials, approval queue, dashboard and tested export formats. High-fidelity operations require specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One bounded goal set and one agent team; final business decisions and external actions remain human-approved.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined agent team package. Offer a monthly operations allowance after repeat demand. Quote complex multi-team or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent operations portal with persistent memory and a live record of agent actions. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run business work autonomously with AI agents that plan, execute and collaborate on tasks. Demonstrate a concrete reviewed agent operations portal with persistent memory and a live record of agent actions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Executives and strategy teams running recurring business work through AI agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed agent operations portal with persistent memory and a live record of agent actions from a small authorized input set, with a transparent calculation of approved agent tasks per operating week and rework after human review and no promised savings.

The first 30 days

1. Week 1: interview five executives and strategy teams running recurring business work through AI agents and inspect a recent example of agent work scattered across rented tools, so goals, approvals and results are hard to coordinate or audit.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure approved agent tasks per operating week and rework after human review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Approved agent tasks per operating week and rework after human review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Approved agent tasks per operating week and rework after human review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent operations portal with persistent memory and a live record of agent actions. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent roles, tool permissions and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for executives and strategy teams running recurring business work through AI agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Verse, SureThing.io and Tycoon AI, plus manual coordination and generic automation tools. Compare this product with the buyer's present method on approved agent tasks per operating week and rework after human review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, agent runtime, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed agent operations portal with persistent memory and a live record of agent actions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve goal intent, source attribution, action accuracy and usage permissions. Named owners approve substantive agent actions and external scope. One bounded goal set and one agent team; final business decisions and external actions remain human-approved. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.