Agent workflow operations control portal

For

IT and operations teams building, deploying and running AI agents that automate business workflows with human oversight

Solves

Agent work is spread across separate builder, trigger, orchestration, approval and cost tools, so teams cannot see or control what runs, what it touches and what it costs.

Delivers

Reviewed agent runs with run receipts and cost logs

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce tool sprawl and ungoverned agent runs while keeping human approval over side-effecting actions.

1. Describe an agent in plain language and generate a draft.
2. Generate no-code apps such as dashboards, portals and CRMs.
3. Connect agents to external services and APIs.
4. Start agents from Slack, Teams or Discord.
5. Run agents when business events fire in connected tools.
6. Run agents on a schedule.
7. Combine agent reasoning with fixed deterministic steps.
8. Coordinate multiple specialist agents on multi-step work.
9. Pause agents and route decisions to people before side-effecting actions.
10. Store context and facts across sessions.
11. Give agents web search and code execution tools.
12. Route between LLM providers and custom APIs.
13. Detect failures, re-plan or retry, and escalate when necessary.
14. Record what each run did, what it cost, what it touched and why it failed.
15. Set per-run and project-level spend and concurrency caps.
16. Run one-to-one voice conversations and capture context.
17. Generate summaries, documents and key points.
18. Create, move and update tickets on project boards.
19. Self-host the platform on own infrastructure.
20. Isolate each agent run in its own workspace.
21. Connect external AI clients through the Model Context Protocol.
22. Compare the reviewed result with the recorded baseline and value assumptions.
23. Capture corrections and named-owner approval before consequential use.
24. Export a versioned reviewed agent run record with source references and unresolved questions.

Everything these tools do, in one app

• Natural language agent builder Lets users describe an agent in plain language and have the platform create it.Found in Keystroke, Budibase AI Agents, Taskade Genesis
• No-code app builder Generates working applications like dashboards, portals, and CRMs without writing code.Found in Taskade Genesis
• Tool and API integrations Connects agents to external services and APIs so they can take actions in other systems.Found in Keystroke, Budibase AI Agents, DeployHermes and 3 more
• Messaging platform triggers Starts agents from chat platforms like Slack, Teams, or Discord where teams already work.Found in Keystroke, Budibase AI Agents
• Event-driven triggers Runs agents automatically when business events fire in connected tools.Found in Keystroke, Triggered Agents by Adaptive
• Scheduled runs Runs agents on a schedule without manual initiation.Found in Keystroke, Warren
• Deterministic workflow steps Combines agent reasoning with fixed, predictable steps in a workflow.Found in Keystroke, Budibase AI Agents
• Multi-agent orchestration Coordinates multiple specialist agents to complete multi-step work together.Found in Keystroke, DeployHermes, Mindra
• Human approval gates Pauses agents and routes decisions to people before side-effecting actions proceed.Found in Keystroke, DeployHermes, Mindra and 1 more
• Agent memory Stores context and facts so agents retain knowledge across sessions and improve over time.Found in Keystroke, DeployHermes, Mindra and 1 more
• Built-in web search and code execution Gives agents tools to search the web and run code as part of their work.Found in Keystroke
• Model routing Lets teams choose and route between multiple LLM providers or custom APIs.Found in Budibase AI Agents
• Self-healing workflows Detects failures, re-plans or retries, and escalates only when necessary.Found in Mindra
• Run receipts and cost logs Records what each run did, what it cost, what it touched, and why it failed.Found in DeployHermes, Warren
• Spend and concurrency limits Sets per-run and project-level caps on cost and parallel execution.Found in Mindra, Warren
• Voice meeting agents Runs one-to-one voice conversations and captures conversational context.Found in muno
• Automated summaries and documents Generates summaries, documents, and key points from conversations or events.Found in muno, Triggered Agents by Adaptive
• Ticket and board automation Creates, moves, and updates tickets on project boards based on outcomes.Found in muno, DeployHermes
• Open-source self-hosting Lets teams inspect the source and run the platform on their own infrastructure.Found in Keystroke, Budibase AI Agents, Warren
• Isolated run workspaces Creates a separate, isolated environment for each agent run.Found in DeployHermes, Warren
• MCP integration Connects external AI clients through the Model Context Protocol to manage agents.Found in Keystroke, DeployHermes

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language agent descriptions, connected tools, business events and approval rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language agent descriptions

4. 3

   Connected tools

5. 4

   Business events and approval rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent runs with run receipts and cost logs

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 fixed approval policy and connected tool set; final authorization and side-effecting 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 workflow canvas, Run monitor and approval queue, Receipts and cost log. Use a project list for agents and workflows, a large central canvas for steps and triggers, and a right-hand panel for tools, models, limits and comments. Let users compare draft and published versions side by side. Display draft, changes requested, approved and running states. Provide a client preview link with comments anchored to the relevant step. Make the task-specific outcome reviewed agent runs with run receipts and cost logs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, connected tool credentials, approval states, usage allowances, run 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

Customer-owned agent descriptions, authorized tool credentials and permitted event sources. Cloud asset storage, design-file import/export and publishing destinations. 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: describe an agent in plain language and generate a draft; connect agents to external services and APIs. 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

   2 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 IT and operations teams building, deploying and running AI agents that automate business workflows with human oversight use it to solve "agent work is spread across separate builder, trigger, orchestration, approval and cost tools, so teams cannot see or control what runs, what it touches and what it costs"?
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 runs per operator hour and unapproved side-effecting actions.
4. Measure, then decide. Track approved agent runs per operator hour and unapproved side-effecting actions; 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 fixed approval policy and connected tool set; final authorization and side-effecting actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: describe an agent in plain language and generate a draft; connect agents to external services and APIs. Support the third module with operator review: run agents when business events fire in connected tools. 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 runs with run receipts and cost logs. Retain the explicit scope boundary: One fixed approval policy and connected tool set; final authorization and side-effecting actions remain human.

What the build depends on. Agent upload and preview, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed approval policy and connected tool set; final authorization and side-effecting 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 agent package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or voice work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent runs with run receipts and cost logs. 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 tool sprawl and ungoverned agent runs while keeping human approval over side-effecting actions. Demonstrate a concrete reviewed agent runs with run receipts and cost logs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and operations teams building, deploying and running AI agents that automate business workflows with human oversight 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 runs with run receipts and cost logs from a small authorized input set, with a transparent calculation of approved agent runs per operator hour and unapproved side-effecting actions and no promised savings.

The first 30 days

1. Week 1: interview five IT and operations teams building, deploying and running AI agents that automate business workflows with human oversight and inspect a recent example of agent work spread across separate builder, trigger, orchestration, approval and cost tools.
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 runs per operator hour and unapproved side-effecting actions, 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 runs per operator hour and unapproved side-effecting actions. 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 runs per operator hour and unapproved side-effecting actions; 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 runs with run receipts and cost logs. 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 patterns, connected tool configurations 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 IT and operations teams building, deploying and running AI agents that automate business workflows with human oversight. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Keystroke, Budibase AI Agents, DeployHermes, Mindra, muno, Triggered Agents by Adaptive, Warren and Taskade Genesis. Compare this product with the buyer's present method on approved agent runs per operator hour and unapproved side-effecting actions. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, code execution, 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 runs with run receipts and cost logs. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, credential boundaries and usage permissions. Named owners approve substantive changes and side-effecting actions. One fixed approval policy and connected tool set; final authorization and side-effecting actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.