Traditional GenAI firewalls only filter model outputs. Aigentical intercepts the tool calls, API integrations, and system actions in the execution path — and blocks the unauthorized ones before they run.
Deny-by-default stopping an ungoverned tool call, a destructive action held for cryptographic human approval, and the tamper-evident record that maps to the EU AI Act, NIST AI RMF, and AIUC-1 — the standard insurers underwrite AI agents against.
Unlike a chatbot, an agent doesn't just answer questions; it takes actions against your systems — refunds, deletes, production changes, one agent delegating to another. Aigentical decouples the policy decision from the agent runtime: a zero-trust architecture for the non-human identities already running across your environment, enforced at every call.
Non-human identities already outnumber humans (CyberArk, 2025). AI agents are the fastest-growing class, and the only one that takes actions.
A startup watched an agent delete its production database in nine seconds. An attacker planted system-wiping commands in Amazon's own coding agent. Amazon shipped them in a public release. This isn't hypothetical anymore.
They run on Copilot Studio, LangChain, CrewAI, Bedrock, and Vertex, across every cloud — and as agent marketplaces emerge, you'll increasingly run agents you didn't write.
Every platform ships its own half-answer to "governance." Approvals, allowlists, and audit splinter across tools, and no one can say what any single agent actually did.
The EU AI Act requires logging and human oversight. AIUC-1 — the AI-agent insurance standard, now with its first accredited auditor — is what insurers grade you on. Both come down to one thing: govern the action, keep the record.
Enforcement is the mechanism; these are the outcomes it produces — the ones your security, compliance, and finance teams each need to see.
Proof for SOC 2, the EU AI Act, and ISO 42001 accrues as your agents run. No screenshots, no scramble the week before an audit.
Every action lands in one place your board and your auditors can actually read, not a pile of gateway logs to reconcile.
AI-agent insurers grade you on controls like these. We hold that grade every day, not just on the morning of the review.
Aigentical isn't software you watch run. It's the layer that lets you prove your AI did only what it was allowed to do.
Most teams can name maybe half the AI running inside them. Aigentical finds the rest before a single policy runs, surfacing every agent and AI call on your network or under your provider accounts, including the shadow AI nobody set up.
Six angles, one reconciled inventory. Start in monitor mode and watch first, then turn governance on per agent, per tool, per risk tier. Total visibility, selective enforcement.
Chat-era tools were built for text in, text out. The real risk now lives in what an agent does after the model speaks: the tool calls, the deletes, the refunds, the handoffs. That is the layer Aigentical runs on.
Low-risk actions run in-path. Policy evaluation adds microseconds.
Sensitive actions freeze until authorized by a signed, single-use human token, verified server-side.
Every action is logged with a cryptographic receipt in a tamper-evident chain.
An attacker has to beat every layer that runs on the surface they are on.
Six insertion surfaces, one stateful pipeline: SDK · decorator · proxy · MCP · K8s admission webhook · sidecar. Add a surface, not a rewrite.
Firewalls, IAM, and DLP were built for people and packets. None of them hold a non-human identity's reputation, weigh the weakest link in a chain of delegations, or issue an approval a fast, automated process can't forge. These are the primitives that do. The evolutionary step between IAM and what agents actually do at runtime.
Standard human-in-the-loop workflows are decoupled from execution, leaving them open to state manipulation. Aigentical treats human authorization as a strict cryptographic primitive: high-risk tool execution is physically blocked until a single-use, time-bound authorization token is verified server-side. The agent has no material to forge it.
Eleven of these are patent-pending. The controls agent governance needs that IAM, DLP, and network firewalls were never built to provide.
Authentication is the front door. The risk is the action. Aigentical consumes the identity signal you already issue and makes it enforceable at every single call.
No rip-and-replace. We sit on top of the IAM you already run and make it enforceable at every agent action.
Human IAM authenticates once at the door. We compute the agent's effective permission per action, layered on top of the identity you already issue.
You already run guardrails, and probably a gateway or two. We don't replace them. We're the single Policy Decision Point (PDP) they all report into, so nothing you've bought goes to waste, and nothing about governance fragments across them.
Self-hardening: every catch, yours or a detector's, feeds reputation, mosaic, and pattern capture, so the system gets harder to attack the longer it runs. Inside your tenant, never shared.
Works alongside LiteLLM · Kong · Bifrost · Portkey · Gravitee, or whatever you run. The textbook pattern: many enforcement points, one decision point (NIST 800-207).
One operating loop, from the first scan to audit-ready evidence your board can read.
Surface every agent and AI call from real traffic.
Connect tool sources; bind policies to the tools that matter.
Enforce in-path: allow, approve, or deny, every action.
Every decision lands in a tamper-evident chain, streamed to your SIEM.
Map the evidence to SOC 2, EU AI Act, TRiSM, AEGIS, AIUC-1, and more.
AI-agent assurance is now a standard, not a slide. AIUC-1 — the insurance-grade standard for AI agents from AIUC, developed with Anthropic and MITRE and now audited by Schellman — sets the bar for governing what agents do. Aigentical maps to it.
Below, the obligations your program is measured against, each paired with the specific Aigentical control that satisfies it: the runtime-governance model analysts describe (Gartner AI TRiSM, Forrester AEGIS), stated as controls, not positioning.
The control auditors look for is enforcement as the action happens — allow, hold, or deny in the execution path — not a review after the fact.
Every decision — allowed, held, denied — is written to an HMAC-chained, tamper-evident log and streamed to your SIEM.
Cryptographic human-in-the-loop on high-risk actions, per-agent blast radius, and least-privilege allowlists. The controls behind AI-agent insurability.
TRiSM, AEGIS, and AIUC-1 are frameworks and standards of their respective owners. Mappings shown are Aigentical's own and do not imply review, endorsement, or certification by Gartner, Forrester, or AIUC.
I built Aigentical after watching teams wire AI agents into production with real credentials and no way to say no at the moment an action fires. Every “governance” answer I found reviewed things after the fact, or trusted the agent to behave. So we built the part that decides in-line — and keeps a record you can hand to an auditor. If you're putting agents anywhere near systems that matter, I'd genuinely like to hear what you're up against.
— SANGEET RAJAN · FOUNDER, AIGENTICAL
Map your agent inventory, point the proxy at staging with no code changes, and watch governance run on your own traffic before you commit to anything. We deploy it with you and hand you the evidence, not a login and a wiki.