AI app for it and development · no coding needed
Agent action screening and governance portal
Reduce ungoverned agent actions while keeping a reviewable record of every block and approval.
Made for: Platform and security teams operating AI agents that call tools and internal systems
What it does for you
The problem
Agent inputs, retrieved content and tool calls reach models and internal APIs without consistent screening, policy checks or auditable decisions.
What it gives you
Screened, policy-checked and logged actions linked to named-owner decisions
What you give it
Agent configurationspolicy rulesprovider credentialsinternal API specifications
Build your own version of Koreshield, ElevenAgents Guardrails 2.0 and more
One app with what these 4 AI tools do, yours to keep and change: Koreshield, ElevenAgents Guardrails 2.0, SolonGate, TrueFoundry AI Gateway.
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
How it works, step by step
- Screen incoming prompts before they reach the model
- Check every proposed tool call against policy and block violations
- Scan retrieved documents and knowledge base content for hidden instructions
- Validate agent output before it reaches end users
- Apply declarative and rules-based policies to agent behavior
- Record each request and block decision with evidence for auditing
- Support multiple LLM providers without per-provider configuration changes
- Run detect mode to flag suspicious inputs without blocking
- Allow custom guardrails in natural language as independent parallel checks
- Provide pre-built protections for content safety, focus and prompt injection
- Define configurable exit strategies when a policy is violated
- Redact conversation history for compliance-sensitive deployments
- Offer zero retention mode where no conversation data is retained
- Inspect each action against authorization, rule and risk layers
- Evaluate risk of actions not hard-blocked by policy in an isolated judge
- Return structured denial responses naming the denial layer and reason
- Intercept actions inline between LLMs and internal APIs
- Provide a unified API control plane for models, MCPs, guardrails, prompts and agents
- Trace prompts, completions, tool call results, latency and time-to-first-token
- Apply request volume limits, cost controls, content guardrails and rate-limiting
- Support multiple MCPs with token and auth management
- Manage and route prompts to simplify model swaps and failover
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Agent action screening and governance portal with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Agent action screening and governance portal with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce ungoverned agent actions while keeping a reviewable record of every block and approval. For platform and security teams operating AI agents that call tools and internal systems, convert incoming prompts, retrieved documents, proposed tool calls and model responses into screened, policy-checked and logged actions linked to named-owner decisions. The benefit is a testable hypothesis, measured through blocked violations per reviewed action and unapproved actions reaching internal systems; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent configurations, policy rules, provider credentials and internal API specifications, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve screened, policy-checked and logged actions linked to named-owner decisions, and measure blocked violations per reviewed action and unapproved actions reaching internal systems against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
