Agent action screening and governance portal

For

Platform and security teams operating AI agents that call tools and internal systems

Solves

Agent inputs, retrieved content and tool calls reach models and internal APIs without consistent screening, policy checks or auditable decisions.

Delivers

Screened, policy-checked and logged actions linked to named-owner decisions

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 ungoverned agent actions while keeping a reviewable record of every block and approval.

1. Screen incoming prompts before they reach the model.
2. Check every proposed tool call against policy and block violations.
3. Scan retrieved documents and knowledge base content for hidden instructions.
4. Validate agent output before it reaches end users.
5. Apply declarative and rules-based policies to agent behavior.
6. Record each request and block decision with evidence for auditing.
7. Support multiple LLM providers without per-provider configuration changes.
8. Run detect mode to flag suspicious inputs without blocking.
9. Allow custom guardrails in natural language as independent parallel checks.
10. Provide pre-built protections for content safety, focus and prompt injection.
11. Define configurable exit strategies when a policy is violated.
12. Redact conversation history for compliance-sensitive deployments.
13. Offer zero retention mode where no conversation data is retained.
14. Inspect each action against authorization, rule and risk layers.
15. Evaluate risk of actions not hard-blocked by policy in an isolated judge.
16. Return structured denial responses naming the denial layer and reason.
17. Intercept actions inline between LLMs and internal APIs.
18. Provide a unified API control plane for models, MCPs, guardrails, prompts and agents.
19. Trace prompts, completions, tool call results, latency and time-to-first-token.
20. Apply request volume limits, cost controls, content guardrails and rate-limiting.
21. Support multiple MCPs with token and auth management.
22. Manage and route prompts to simplify model swaps and failover.

Everything these tools do, in one app

• Input screening Scans incoming user messages or prompts before they reach the model.Found in Koreshield, ElevenAgents Guardrails 2.0
• Tool call governance Checks every tool call the agent proposes against policy before execution and blocks violations.Found in Koreshield, SolonGate
• RAG security Scans retrieved documents or knowledge base content for hidden instructions before they enter the model's context.Found in Koreshield
• Response validation Validates the agent's output before it reaches end users.Found in ElevenAgents Guardrails 2.0
• Policy enforcement Applies declarative or rules-based policies to govern agent behavior and actions.Found in Koreshield, SolonGate, TrueFoundry AI Gateway
• Decision logging Records each request and block decision with evidence for auditing.Found in Koreshield, SolonGate
• Multi-provider support Works across multiple LLM providers without per-provider configuration changes.Found in Koreshield, TrueFoundry AI Gateway
• Detect mode Flags suspicious inputs for review without blocking them, allowing threshold tuning.Found in Koreshield
• Custom guardrails Allows custom rules expressed in natural language, executed as independent parallel checks.Found in ElevenAgents Guardrails 2.0
• Pre-built protections Provides ready-made protections for content safety, focus, and prompt injection.Found in ElevenAgents Guardrails 2.0
• Configurable exit strategies Defines automated responses when a policy is violated.Found in ElevenAgents Guardrails 2.0
• Conversation redaction Redacts conversation history for compliance-sensitive deployments.Found in ElevenAgents Guardrails 2.0
• Zero retention mode Offers a mode where no conversation data is retained, for enterprise compliance.Found in ElevenAgents Guardrails 2.0
• Deterministic policy engine Uses rules-based filtering to inspect each action against authorization, rule, and risk layers.Found in SolonGate
• Isolated AI judge A localized component evaluates the risk profile of actions not hard-blocked by policy.Found in SolonGate
• Structured denial responses Returns a JSON payload naming the denial layer and a plain-language reason for auditability.Found in SolonGate
• Inline interception Sits directly in the communication path between LLMs and internal APIs to intercept actions.Found in SolonGate
• Unified API control plane Provides a single API to connect, observe, and govern models, MCPs, guardrails, prompts, and agents.Found in TrueFoundry AI Gateway
• End-to-end tracing Captures prompts, completions, tool call results, latency, and time-to-first-token for observability.Found in TrueFoundry AI Gateway
• Governance controls Includes request volume limits, cost controls, content guardrails, and configurable rate-limiting and fallback behavior.Found in TrueFoundry AI Gateway
• Multi-MCP support Supports multiple MCPs and token/auth management for different provider auth flows.Found in TrueFoundry AI Gateway
• Prompt management and routing Reduces client-side code and simplifies model swaps and failover.Found in TrueFoundry AI Gateway

What goes in, what comes out

The workflow

1. In
   Start with

   Agent configurations, policy rules, provider credentials and internal API specifications

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent configurations

4. 3

   Policy rules

5. 4

   Provider credentials and internal API specifications

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Screened, policy-checked and logged actions linked to named-owner decisions

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 policy evaluation, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed policy schema and supported provider set; final security and compliance decisions remain with the buyer's qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Policy and guardrail configuration, Live interception and decision log, Review and audit. Use a dashboard for connected models, MCPs and agents, a central stream of intercepted requests with decision state, and a right-hand panel for policy layers, evidence and reviewer notes. Let users compare detect mode against enforce mode side by side. Display allowed, flagged, blocked and approved states. Provide an audit export link with decisions anchored to the relevant request. Make the task-specific outcome screened, policy-checked and logged actions linked to named-owner decisions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, connected models and MCPs, policy versions, reviewer assignments, approval states, usage allowances, retention settings, export 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

Buyer-owned agent frameworks, provider APIs and internal API specifications. Cloud secret storage, identity providers and audit 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: screen incoming prompts before they reach the model; check every proposed tool call against policy and block violations. 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 platform and security teams operating AI agents that call tools and internal systems use it to solve "agent inputs, retrieved content and tool calls reach models and internal APIs without consistent screening, policy checks or auditable decisions"?
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 violations per reviewed action and unapproved actions reaching internal systems.
4. Measure, then decide. Track blocked violations per reviewed action and unapproved actions reaching internal systems; 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 policy schema and supported provider set; final security and compliance decisions remain with the buyer's qualified reviewers. Implement one approved input format, a bounded representative case set and the first two task modules: screen incoming prompts before they reach the model; check every proposed tool call against policy and block violations. Support the remaining modules with operator review: scan retrieved documents and knowledge base content for hidden instructions; validate agent output before it reaches end users. 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 provider integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around screened, policy-checked and logged actions linked to named-owner decisions. Retain the explicit scope boundary: One fixed policy schema and supported provider set; final security and compliance decisions remain with the buyer's qualified reviewers.

What the build depends on. Agent configuration upload, policy editor, asynchronous screening jobs, decision log storage, 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 policy schema and supported provider set; final security and compliance decisions remain with the buyer's qualified reviewers.

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-provider or compliance-sensitive deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded screened, policy-checked and logged actions linked to named-owner decisions. 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 ungoverned agent actions while keeping a reviewable record of every block and approval. Demonstrate a concrete screened, policy-checked and logged actions linked to named-owner decisions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Platform and security teams operating 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 screened, policy-checked and logged actions linked to named-owner decisions from a small authorized input set, with a transparent calculation of blocked violations per reviewed action and unapproved actions reaching internal systems and no promised savings.

The first 30 days

1. Week 1: interview five platform and security teams operating AI agents that call tools and internal systems and inspect a recent example of agent inputs, retrieved content and tool calls reaching models and internal APIs without consistent screening, policy checks or auditable decisions.
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 violations per reviewed action and unapproved actions reaching internal systems, 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 violations per reviewed action and unapproved actions reaching internal systems. 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 violations per reviewed action and unapproved actions reaching internal systems; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs screened, policy-checked and logged actions linked to named-owner decisions. 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, provider configurations and review examples, together with reliable delivery for a narrow governance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for platform and security teams operating AI agents that call tools and internal systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Koreshield, ElevenAgents Guardrails 2.0, SolonGate and TrueFoundry AI Gateway. Compare this product with the buyer's present method on blocked violations per reviewed action and unapproved actions reaching internal systems. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model 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 screened, policy-checked and logged actions linked to named-owner decisions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve policy intent, source attribution, decision accuracy and usage permissions. Named owners approve substantive policy changes and enforcement scope. One fixed policy schema and supported provider set; final security and compliance decisions remain with the buyer's qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.