Agent action control and audit portal

For

Platform, security and IT operations teams running AI agents that connect to internal apps, tools and data

Solves

Agents act across connected systems without a single place to authorize each action, review what happened or stop a bad run.

Delivers

Operator-approved decision record linked to each executed action

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 unauthorized or unreviewed agent actions while keeping a complete record of what each agent touched.

1. Evaluate each agent action against rules and return allow or deny before it runs.
2. Record every agent action with what was touched and whether it was allowed or blocked.
3. Route actions that need a person's decision to a human for approval before continuing.
4. Apply the same controls across multiple agents or frameworks without rebuilding for each.
5. Connect and configure agent controls without writing code.
6. Store and inject credentials securely so agents access tools without seeing secrets.
7. Create and maintain connectors to external tools from API docs, links or descriptions.
8. Test rules in a sandbox that logs decisions without blocking before enforcing them.
9. Send alerts when unauthorized or policy-violating access attempts happen.
10. Give each agent its own key with limited access that can be revoked immediately.
11. Block or rewrite risky actions like destructive commands or credential exfiltration based on configurable rules.
12. Provide a searchable catalog of API specs to find the right operation without custom wrappers.
13. Allow deployment on your own infrastructure for control over data and security.
14. Offer a visual interface to design and manage how multiple agents work together.
15. Learn how work flows through existing apps and screen-based tools to create stable agent interfaces.
16. Detect when underlying UIs change, stop before side effects and repair simple changes automatically.
17. Connect with existing identity management and directory services for permission management.
18. Provide a way to immediately stop agent actions in case of an incident.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned operator-approved decision record linked to each executed action with source references and unresolved questions.

Everything these tools do, in one app

• Agent action authorization Evaluates each agent action against rules and returns an allow or deny decision before it runs.Found in Kastra, CtrlAI, Venn.ai and 1 more
• Activity audit logs Records every agent action with details of what was touched and whether it was allowed or blocked.Found in Venn.ai, Kastra, Portia AI and 6 more
• Human-in-the-loop approval Routes actions that need a person's decision to a human for approval before continuing.Found in Kastra, Portia AI, Graft AI
• Cross-agent compatibility Works with multiple AI agents or frameworks so the same controls apply without rebuilding for each.Found in Venn.ai, Kastra, CtrlAI
• No-code setup Lets users connect and configure agent controls without writing code.Found in Venn.ai, Multi-Agent Builder
• Credential management Stores and injects credentials securely so agents can access tools without seeing secrets.Found in Merge Agent Handler, Mighty, Jentic Mini
• Connector generation Creates and maintains connectors to external tools from API docs, links, or descriptions.Found in Merge Agent Handler, Jentic Mini
• Policy testing modes Lets teams test rules in a sandbox that logs decisions without blocking before enforcing them.Found in Kastra, Graft AI
• Real-time alerts Sends alerts when unauthorized or policy-violating access attempts happen.Found in Merge Agent Handler, Permit AI Access Control
• Scoped agent keys Gives each agent its own key with limited access that can be revoked immediately.Found in Jentic Mini
• Guardrail rule enforcement Blocks or rewrites risky actions like destructive commands or credential exfiltration based on configurable rules.Found in CtrlAI, Venn.ai, Portia AI
• API catalog search Provides a searchable catalog of API specs to find the right operation without custom wrappers.Found in Jentic Mini
• Self-hosting option Allows deployment on your own infrastructure for control over data and security.Found in Jentic Mini, CtrlAI, Portia AI
• Visual workflow design Offers a visual interface to design and manage how multiple agents work together.Found in Multi-Agent Builder
• Operational mapping Learns how work flows through existing apps and screen-based tools to create stable agent interfaces.Found in Graft AI
• Drift detection and repair Detects when underlying UIs change, stops before side effects, and can repair simple changes automatically.Found in Graft AI
• Identity integration Connects with existing identity management and directory services for permission management.Found in Permit AI Access Control
• Emergency kill switch Provides a way to immediately stop agent actions in case of an incident.Found in CtrlAI, Jentic Mini

What goes in, what comes out

The workflow

1. In
   Start with

   Agent action requests, connector definitions, identity data and policy rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent action requests

4. 3

   Connector definitions

5. 4

   Identity data and policy rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Operator-approved decision record linked to each executed action

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 agent framework and connector set; final authorization and incident decisions remain with the security operator. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent and connector registry, Policy and approval console, Action audit and incident view. Use a list of connected agents and tools, a central rule and approval workspace, and a right-hand panel for decision evidence and comments. Let users compare policy versions side by side. Display allowed, denied, pending approval and killed states. Provide a searchable audit view with each decision anchored to the action it governed. Make the task-specific outcome operator-approved decision record linked to each executed action visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, agent and connector versions, operator comments, approval states, usage allowances, revocation 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 frameworks, identity providers and permitted internal tools. Cloud or self-hosted deployment, API catalog import/export and alerting destinations. Start with file exchange and validate destination specifications before promising direct enforcement. 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: evaluate each agent action against rules and return allow or deny before it runs; record every agent action with what was touched and whether it was allowed or blocked. 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 platform, security and IT operations teams running AI agents that connect to internal apps, tools and data use it to solve "agents act across connected systems without a single place to authorize each action, review what happened or stop a bad run"?
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: Blocked unauthorized actions per review hour and unapproved side effects after enforcement.
4. Measure, then decide. Track blocked unauthorized actions per review hour and unapproved side effects after enforcement; 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 agent framework and connector set; final authorization and incident decisions remain with the security operator. Implement one approved input format, a bounded representative case set and the first two task modules: evaluate each agent action against rules and return allow or deny before it runs; record every agent action with what was touched and whether it was allowed or blocked. Support the third module with operator review: route actions that need a person's decision to a human for approval before continuing. 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 operator-approved decision record linked to each executed action. Retain the explicit scope boundary: One fixed agent framework and connector set; final authorization and incident decisions remain with the security operator.

What the build depends on. Agent and connector registry, asynchronous evaluation jobs, editable policy history, reviewer access and tested export formats. High-fidelity enforcement requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent framework and connector set; final authorization and incident decisions remain with the security operator.

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 and connector package. Offer a monthly operations allowance after repeat demand. Quote complex multi-agent or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded operator-approved decision record linked to each executed action. 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 unauthorized or unreviewed agent actions while keeping a complete record of what each agent touched. Demonstrate a concrete operator-approved decision record linked to each executed action using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Platform, security and IT operations teams running 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 operator-approved decision record linked to each executed action from a small authorized input set, with a transparent calculation of blocked unauthorized actions per review hour and unapproved side effects after enforcement and no promised savings.

The first 30 days

1. Week 1: interview five platform, security and IT operations teams running AI agents that connect to internal apps, tools and data and inspect a recent example of agents acting across connected systems without a single place to authorize each action, review what happened or stop a bad run.
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 blocked unauthorized actions per review hour and unapproved side effects after enforcement, 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: Blocked unauthorized actions per review hour and unapproved side effects after enforcement. 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

Blocked unauthorized actions per review hour and unapproved side effects after enforcement; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs operator-approved decision record linked to each executed action. 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 policies, connector mappings and review examples, together with reliable delivery for a narrow operational niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for platform, security and IT operations teams running AI agents that connect to internal apps, tools and data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Venn.ai, Kastra, Portia AI, Merge Agent Handler, Mighty, Jentic Mini, Permit AI Access Control, CtrlAI, Multi-Agent Builder and Graft AI, plus manual scripts and internal tools. Compare this product with the buyer's present method on blocked unauthorized actions per review hour and unapproved side effects after enforcement. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, connector maintenance, 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 operator-approved decision record linked to each executed action. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve least privilege, source attribution, action accuracy and usage permissions. Security operators approve substantive changes and enforcement scope. One fixed agent framework and connector set; final authorization and incident decisions remain with the security operator. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.