AI agent action guard and audit workspace

For

Security and platform engineers running AI agents and MCP-connected tools inside their own systems

Solves

AI agents and their tool calls can execute risky commands, leak secrets or be manipulated by prompt injection, and teams lack one place to intercept, decide, audit and report on those actions.

Delivers

Reviewed allow, block or approval decisions with evidence tied to a specific agent version

Built in

about 4 weeks of creation time, MVP in 5 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 exposure to risky AI agent actions while keeping a reviewable record of every decision.

1. Intercept AI agent tool calls before execution.
2. Detect prompt injection, credential theft and unauthorized code execution.
3. Apply fixed policy rules to block or allow each call.
4. Request human approval for ambiguous high-impact actions.
5. Parse commands to separate execution, dry runs and quoted examples.
6. Proxy and scan traffic between AI apps and the system.
7. Run entirely on the user's machine without external data transfer.
8. Store local evidence of risky calls, matched rules and decisions.
9. Maintain hundreds of heuristics for package safety, secret exfiltration and destructive filesystem or database operations.
10. Send real-time alerts and let users control access.
11. Keep a searchable log history of past alerts.
12. Simulate the agent's workflows, roles, rules, permissions and tool calls.
13. Run independent AI judging on models the audited agent never uses.
14. Score finding severity from simulation context and misalignment category.
15. Map gaps to EU AI Act, NIST AI RMF, OWASP Top 10 and ISO/IEC
16. Export an evidence report tied to a specific agent version.
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 allow, block or approval decisions with evidence tied to a specific agent version with source references and unresolved questions.

Everything these tools do, in one app

• Real-time action interception Stops or inspects AI agent actions before they are executed.Found in HOL Guard, MCP Defender
• Threat detection Identifies malicious activities such as prompt injection, credential theft, and unauthorized code execution.Found in HOL Guard, MCP Defender
• Block or allow control Lets users or policies block or allow suspicious tool calls.Found in HOL Guard, MCP Defender
• Local-only operation Runs entirely on the user's machine without sending data externally.Found in HOL Guard
• Deterministic policy enforcement Applies fixed security rules without relying on live model reasoning for decisions.Found in HOL Guard
• Structured command parsing Distinguishes between execution, dry runs, and quoted examples to reduce false alarms.Found in HOL Guard
• Three-tier action handling Automatically allows known-safe work, blocks clear threats, and requests approval for ambiguous high-impact actions.Found in HOL Guard
• Local evidence storage Stores analytics showing which tool calls were risky, why a rule matched, and whether actions were blocked or approved.Found in HOL Guard
• Security heuristics Uses hundreds of rules covering package safety, prompt injection, secret exfiltration, and catastrophic filesystem or database operations.Found in HOL Guard
• Open-source codebase Allows teams to audit and modify the rules, parsers, and test corpus.Found in HOL Guard, MCP Defender
• Automatic traffic proxying Proxies and scans communications between AI apps and the system.Found in MCP Defender
• Real-time alerts Notifies users of suspicious behavior and allows them to control access.Found in MCP Defender
• LLM-based scanning Uses large language models to identify potential threats.Found in MCP Defender
• Log history Provides a record of past alerts for review.Found in MCP Defender
• Multi-platform support Works on multiple operating systems, with current and planned support.Found in MCP Defender
• AI agent auditing Runs audits to assess AI agents for misalignment and failures.Found in iFixAi
• Simulation environment Models the agent's expected workflows, roles, rules, permissions, and tool calls.Found in iFixAi
• Independent AI judging Evaluates results using models the agent under audit never runs on.Found in iFixAi
• Compliance mapping Maps identified gaps to frameworks like EU AI Act, NIST AI RMF, OWASP Top 10, and ISO/IEC 42001.Found in iFixAi
• Severity scoring Assigns weighted severity levels to findings based on simulation context and misalignment category.Found in iFixAi
• Evidence reporting Produces concrete evidence tied to specific agent versions for engineers to act on.Found in iFixAi

What goes in, what comes out

The workflow

1. In
   Start with

   Agent tool calls, local traffic, policy rules and audit simulations

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent tool calls

4. 3

   Local traffic

5. 4

   Policy rules and audit simulations

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed allow, block or approval decisions with evidence tied to a specific agent version

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 version and rule set; final security decisions and compliance judgments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Policy and rules, Live action review, Audit and evidence report. Use a queue of intercepted tool calls, a large central detail view showing the parsed command, matched rule and proposed decision, and a right-hand panel for policy, simulation context and comments. Let users compare a dry run against an execution and a quoted example. Display allowed, blocked, approval requested and approved states. Provide a client or auditor preview link with findings anchored to the relevant agent version. Make the task-specific outcome reviewed allow, block or approval decisions with evidence tied to a specific agent version visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, rule versions, agent versions, reviewer comments, approval states, usage allowances, alert 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

Agent-owned tool calls, authorized local traffic and permitted policy sources. Local log storage, SIEM export and ticketing 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

   5 days

   One buyer segment, one recurring use case; first modules: intercept AI agent tool calls before execution; detect prompt injection, credential theft and unauthorized code execution. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 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 security and platform engineers running AI agents and MCP-connected tools inside their own systems use it to solve "AI agents and their tool calls can execute risky commands, leak secrets or be manipulated by prompt injection, and teams lack one place to intercept, decide, audit and report on those actions"?
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 risky actions before execution and reviewer time per resolved alert.
4. Measure, then decide. Track blocked risky actions before execution and reviewer time per resolved alert; 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 version and rule set; final security decisions and compliance judgments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: intercept AI agent tool calls before execution; detect prompt injection, credential theft and unauthorized code execution. Support the third module with operator review: apply fixed policy rules to block or allow each call. 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 allow, block or approval decisions with evidence tied to a specific agent version. Retain the explicit scope boundary: One fixed agent version and rule set; final security decisions and compliance judgments remain human.

What the build depends on. Agent call capture and preview, asynchronous scanning jobs, editable rule history, reviewer access and tested export formats. High-fidelity security review requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent version and rule set; final security decisions and compliance judgments 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 regulated compliance work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed allow, block or approval decisions with evidence tied to a specific agent version. 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 exposure to risky AI agent actions while keeping a reviewable record of every decision. Demonstrate a concrete reviewed allow, block or approval decisions with evidence tied to a specific agent version using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Security and platform engineers running AI agents and MCP-connected tools inside their own systems professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed allow, block or approval decisions with evidence tied to a specific agent version from a small authorized input set, with a transparent calculation of blocked risky actions before execution and reviewer time per resolved alert and no promised savings.

The first 30 days

1. Week 1: interview five security and platform engineers running AI agents and MCP-connected tools inside their own systems and inspect a recent example of AI agents and their tool calls can execute risky commands, leak secrets or be manipulated by prompt injection, and teams lack one place to intercept, decide, audit and report on those actions.
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 risky actions before execution and reviewer time per resolved alert, 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 risky actions before execution and reviewer time per resolved alert. 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 risky actions before execution and reviewer time per resolved alert; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed allow, block or approval decisions with evidence tied to a specific agent version. 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 rules, agent configurations and review examples, together with reliable delivery for a narrow security niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for security and platform engineers running AI agents and MCP-connected tools inside their own systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

HOL Guard, MCP Defender and iFixAi, plus manual review and generic logging. Compare this product with the buyer's present method on blocked risky actions before execution and reviewer time per resolved alert. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Interception and scanning compute, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed allow, block or approval decisions with evidence tied to a specific agent version. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve agent version, source attribution, rule accuracy and usage permissions. Security owners approve substantive changes and enforcement scope. One fixed agent version and rule set; final security decisions and compliance judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.