Operations agent orchestration control room

For

Operations leads and delivery managers running recurring back-office and client workflows

Solves

Repetitive operational work is spread across disconnected tools, so risks, approvals and follow-ups depend on manual chasing.

Delivers

Reviewed agent actions with a tamper-evident audit trail

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce manual coordination while keeping humans in control of consequential actions.

1. Build and manage AI agents without code.
2. Let agents learn to operate authorized software tools.
3. Automate complete business processes through customizable flows.
4. Monitor operational data to flag risks before escalation.
5. Identify opportunities from operational data.
6. Pause actions until a human approves them.
7. Record a tamper-evident audit trail of what happened and why.
8. Learn business context, key personnel and past decisions over time.
9. Define escalation paths and data validation rules.
10. Pause and request human input when confidence is low.
11. Embed agents on websites to qualify leads into a CRM.
12. Provide real-time data access for dynamic task execution.
13. Schedule tasks to run autonomously at set times.
14. Analyze activity across tools to find repetitive tasks.
15. Suggest workflows based on each user's context.
16. Draft routine updates and convert notes into tasks and planning docs into tickets.
17. Handle resourcing, chase missing timesheets and identify uninvoiced hours.
18. Assist with documentation, data migrations and environment configuration.
19. Set configurable autonomy levels per agent.

Everything these tools do, in one app

• No-code agent creation Allows users to build and manage AI agents without writing code.Found in Onpilot, O-mega
• AI agents learn tools Agents can learn to operate software tools and manage workflows autonomously.Found in O-mega
• Automate business processes Automates complete business processes through customizable flows.Found in O-mega
• Proactive risk detection Monitors operational data to flag risks and recommend actions before issues escalate.Found in Onpilot, Nitro by Rocketlane
• Opportunity identification Uncovers opportunities by analyzing operational data.Found in Onpilot, Nitro by Rocketlane
• Human approval workflows Pauses actions until a human approves them.Found in Onpilot, Nitro by Rocketlane
• Audit trails Records a tamper-evident audit trail of what happened and why.Found in Onpilot
• Organizational memory Learns business context, key personnel, and past decisions over time to reduce irrelevant alerts.Found in Onpilot
• Exception handling rules Users define escalation paths and data validation rules; AI pauses and requests human input when confidence is low.Found in Onpilot
• Customer-facing lead qualification Embeds agents on websites to guide visitors through questions and push qualified lead data into a CRM.Found in Onpilot
• Real-time data access Provides real-time data access for dynamic task execution.Found in O-mega
• Scheduled task execution Schedules tasks to run autonomously at set times.Found in Onpilot, O-mega
• Automated discovery of repetitive tasks Analyzes activity across tools to find repetitive tasks and hidden structures.Found in Panorama
• Personalized automation recommendations Suggests workflows based on each user's context.Found in Panorama
• Draft and convert artifacts Drafts or converts recurring artifacts like routine updates, meeting notes into tasks, and planning docs into tickets.Found in Panorama
• Back-office automation Handles resourcing tasks, chases missing timesheets, and identifies uninvoiced hours.Found in Nitro by Rocketlane
• Work automation Assists with documentation, data migrations, and environment configuration.Found in Nitro by Rocketlane
• Configurable autonomy levels Allows teams to choose how much independence each agent has.Found in Nitro by Rocketlane

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized tool activity, operational records and approval rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized tool activity

4. 3

   Operational records and approval rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed agent actions with a tamper-evident audit trail

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. One authorized tool set and approval policy; final operational decisions and external actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent builder and autonomy settings, Live operations queue, Approval and audit log. Use a thumbnail gallery for agents and workflows, a large central queue of proposed and running tasks, and a right-hand panel for context, risk flags and comments. Let users compare agent proposals side by side. Display draft, awaiting approval, running, paused and completed states. Provide a client-facing lead capture view with comments anchored to the relevant record. Make the task-specific outcome reviewed agent actions with a tamper-evident audit trail visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, client comments, approval states, autonomy levels, usage allowances, revision limits, download 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

Authorized operational tools, CRM systems and permitted data sources. Cloud storage, ticketing and messaging destinations. Start with file exchange and validate destination specifications before promising direct execution. 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: build and manage AI agents without code; let agents learn to operate authorized software tools. 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 operations leads and delivery managers running recurring back-office and client workflows use it to solve "repetitive operational work is spread across disconnected tools, so risks, approvals and follow-ups depend on manual chasing"?
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 actions per operations hour and manual follow-ups avoided.
4. Measure, then decide. Track approved agent actions per operations hour and manual follow-ups avoided; 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 authorized tool set and approval policy; final operational decisions and external actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: build and manage AI agents without code; let agents learn to operate authorized software tools. Support the third module with operator review: automate complete business processes through customizable flows. 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 actions with a tamper-evident audit trail. Retain the explicit scope boundary: One authorized tool set and approval policy; final operational decisions and external actions remain human.

What the build depends on. Agent builder, live operations queue, approval workflow, audit log and tested export formats. High-fidelity operations require specialist process QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One authorized tool set and approval policy; final operational decisions and external actions remain human.

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 operations package. Offer a monthly production allowance after repeat demand. Quote complex multi-tool or specialist operations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent actions with a tamper-evident audit trail. 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

Reduce manual coordination while keeping humans in control of consequential actions. Demonstrate a concrete reviewed agent actions with a tamper-evident audit trail using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations leads and delivery managers running recurring back-office and client workflows 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 actions with a tamper-evident audit trail from a small authorized input set, with a transparent calculation of approved agent actions per operations hour and manual follow-ups avoided and no promised savings.

The first 30 days

1. Week 1: interview five operations leads and delivery managers running recurring back-office and client workflows and inspect a recent example of repetitive operational work spread across disconnected tools, so risks, approvals and follow-ups depend on manual chasing.
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 actions per operations hour and manual follow-ups avoided, 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 actions per operations hour and manual follow-ups avoided. 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 actions per operations hour and manual follow-ups avoided; 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 actions with a tamper-evident audit trail. 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 configurations, operational constraints 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 operations leads and delivery managers running recurring back-office and client workflows. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Onpilot, O-mega, Panorama and Nitro by Rocketlane, plus manual coordination and generic automation tools. Compare this product with the buyer's present method on approved agent actions per operations hour and manual follow-ups avoided. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Agent runs, tool API calls, 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 actions with a tamper-evident audit trail. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve operational accuracy, source attribution, approval accuracy and usage permissions. Operations leads approve substantive actions and external scope. One authorized tool set and approval policy; final operational decisions and external actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.