Your agent just read 847 files in 4 minutes.
Enterprises are deploying autonomous agents with valid credentials and no layer between those credentials and everything they can reach. AgentGate is that layer — the runtime authorization infrastructure every agent action passes through before it executes.
Your logs show 847 authorized requests. AgentGate saw a kill chain. The difference between a normal run and a data exfiltration is the sequence — and only AgentGate catches it before it fires.
$ python demo.py AgentGate PDP — Trust Authorization Layer ─────────────────────────────────────────[REGISTER] agent_id=analyst_001 purpose="Summarize quarterly business reports"[TOKEN] issued: eyJhbGciOiJFZERTQSJ9... (Ed25519 JWT — scope embedded + signed)[REQUEST] action=read resource=/reports/q1.pdf → PERMIT (trust=0.91)[REQUEST] action=read resource=/reports/q2.pdf → PERMIT (trust=0.89)[REQUEST] action=read resource=/reports/q3.pdf → PERMIT (trust=0.87)[REQUEST] action=read resource=/reports/q4.pdf → PERMIT (trust=0.86)...6 more reads in under 5 minutes...[REQUEST] action=export resource=/reports/*[KILL CHAIN] *** BULK_READ_THEN_EXFIL detected *** 10 reads in 4m32s followed by export attempt Pattern: data enumeration → exfiltration[DECISION] *** DENY ***[REASON] Kill chain: bulk read then exfiltration sequence. No single request triggered this. The sequence did.[AUDIT] entry #4821 — HMAC-chained, tamper-evident[ALERT] security team notified instantly
The Missing Layer
The agent economy needed this layer before you deployed your first agent.
Every major technology shift that required trust at scale produced one infrastructure layer that made trust possible. The agent economy is no different.
The internet needed SSL
before e-commerce could scale.
Every transaction required a trust layer no one had built yet. SSL was that layer — invisible infrastructure that made commerce possible.
Autonomous agents need the same layer before they can act at scale with accountability.
Payments needed Stripe
before the online economy could work.
The payment layer existed. It just took months, compliance teams, and bank relationships to wire up. Stripe made it 3 lines.
Every team deploying agents needs runtime authorization. AgentGate makes it 3 lines.
Identity needed Auth0
before every app could have secure login.
OAuth existed. JWT existed. But building identity correctly was hard enough that most teams got it wrong. Auth0 made it the infrastructure layer — not the project.
Agent authorization exists today only as something you build yourself. AgentGate is the infrastructure layer that replaces that build.
Market Architecture
Four layers of AI agent infrastructure. One is unowned.
Well-funded companies own L1, L2, and L3. They observe, manage identity, and protect the model. None of them answer the one question that matters at runtime.
Protects the AI model from adversarial inputs and poisoned training.
Manages who the agent is and what credentials it holds.
Discovers agents, maps permissions, surfaces risk after the fact.
Should this specific action, by this specific agent, in this specific sequence, execute right now?
Palo Alto acquired Portkey (April 2026) to build into L4. Their product: limited preview, enterprise-only, closed-source.
AgentGate: open source, 3 lines to integrate, shipped, running today.
What trust means precisely
Trust is not a feeling. It is three verifiable claims made simultaneously.
Remove any one of them and trust collapses. An agent with authority but no alignment is a rogue employee with a valid badge. An agent with alignment but no provable record is unverifiable — you believe it, but you can't prove it. Real trust requires all three, enforced continuously, action by action.
Verifiable authority
This agent was legitimately delegated to take this action — through a cryptographically signed chain, scope-attenuated at every hop, traceable back to the authorizing principal.
Behavioral alignment
This action is consistent with why the agent was created. Not just permitted by a policy — semantically aligned with the declared purpose the agent was registered with.
Provable actions
This decision is sealed before execution, tamper-evident, and independently verifiable by any auditor — without trusting the infrastructure that produced it.
AgentGate enforces all three. Every action. Before it executes.
The problem in plain English
Autonomous agents operate faster than any human can audit. By the time your logs flag a problem, the data is already gone — or the action already executed. Traditional access controls were built for humans logging in, not for agents that make hundreds of decisions per minute on your behalf.
AgentGate changes the sequence. Instead of logging what agents did, it authorizes what agents cando — in real time, before execution. It understands context your logs never will: not just that an agent read a file, but what it read before, where it's trying to go next, and whether that pattern looks like legitimate work or an attack in progress.
Scenario
A data analysis agent reads quarterly reports — permitted.
It reads HR records — still permitted individually.
It tries to export everything at once — blocked.
No single action was unauthorized. The sequence was. AgentGate blocked the export before it fired — with zero changes to the agent code.
AgentGate intercepts before execution
Every agent action is scored across four dimensions before it's allowed to run. No agent bypasses the gate.
Identity Verification
25%Ed25519 JWT tokens with scope embedded in the signed credential — immutable after issuance, offline-verifiable with the public key. No database lookup required.
Delegation Chain Integrity
25%Full chain traversal at every authorization call: every ancestor's scope is verified. Trust ceiling prevents trust-washing — a low-trust parent can never spawn a high-trust child. Atomic revoke_chain neutralizes an agent and all descendants in one call.
Purpose Alignment
30%Embedding-based semantic scoring: action + resource (85% weight) vs. declared purpose. Justification is capped at 15% — cannot be used to bypass a misaligned action.
Behavioral Anomaly Detection
20%Per-agent velocity baselines with trust decay over time. Dormancy followed by sudden high-volume activity is itself a risk signal — no static thresholds.
Kill Chain Detection
Beyond single-requestEach individual request may look clean. AgentGate examines the full 24-hour sequence. Bulk reads followed by an export. A read followed by a delete on the same resource. Progressive sensitivity escalation. Directory sweeps across 6+ prefixes. Patterns that only become visible across multiple calls — and that no rule-based system can catch.
Behavioral Contracts
Hard overridesAgents declare hard limits at registration time. Violations always result in DENY — regardless of trust score. An agent that said it would never exceed 10 RPM gets hard-denied the moment it does. An agent authorized only for business hours gets blocked the moment it acts outside them. No probabilistic scoring. No bypass. The agent's own declaration is used against it.
Add one import — authorize before each action
from agentgate import AgentGate
gate = AgentGate("http://localhost:8000", api_key="your-key")
gate.register("report_bot", "ReportBot", "Summarize quarterly reports",
authorized_resources=["/reports/*"], authorized_actions=["read"])
# Before every action:
result = gate.authorize("read", "/reports/q3.pdf") # → PERMIT | ESCALATE | DENYSee every agent action in real time — attacks blocked live
Trust Score
0.91
Decision
PERMIT
Attack Flags
0
Real-time authorization dashboard — watch your agents in the demo video above
See AgentGate in action
Watch a live run — real agents, real attacks, real-time blocking.
What happens when the first line isn't enough
Trust scoring stops most attacks before they execute. These four layers handle everything else — hostile tool responses, lateral compromise, and audit integrity.
Output Sanitization
Every byte your agent receives is scanned before it processes it. Credentials, PII, and exfil URLs are redacted in-place. Accidental leaks from upstream services never reach your agent — or your logs.
Hostile Tool Response Defense
External tools can embed instructions inside their responses to hijack your agent's behavior — a technique known as tool poisoning. AgentGate scans every tool response before the agent processes it. Injection patterns trigger a hard block; credential leaks are redacted in-place.
Trust Contagion
Compromise doesn't stay isolated. When an agent is quarantined, its delegation neighbors are automatically penalized: a rogue child implicates the parent that spawned it; a compromised parent contaminates every child it controls.
Tamper-Evident Audit Trail
Every authorization decision is sealed into a SHA-256 Merkle tree. Any post-hoc modification is cryptographically detectable. O(log n) inclusion proofs are available on demand — compliance reporting without trusting the infrastructure.
Both layers active simultaneously — no configuration required
Why enterprises can't wait
The regulatory and deployment landscape is converging. Authorization controls that were optional in 2024 become hard requirements in 2026.
OWASP LLM06
Excessive Agency — agents granted permissions beyond their declared scope, acting outside their intended purpose. Listed as a critical risk in OWASP Top 10 for LLM Applications.
OWASP Top 10 for LLM Applications, 2025
MITRE ATLAS
Adversarial ML tactics against AI systems now formally catalogued — reconnaissance, privilege escalation, and data exfiltration all apply to autonomous agents.
MITRE ATLAS, 2024
August 2026
EU AI Act high-risk obligations take effect — enterprises have months, not years, to implement governance controls for high-risk AI systems.
EU AI Act (Regulation 2024/1689)
Regulatory pressure and adversarial sophistication are converging. Teams without agent governance controls today face compliance exposure by Q4 2026.
Works with your existing stack
Drop-in integration. No framework changes. No rewrites.
from agentgate import AgentGate gate = AgentGate("http://localhost:8000", api_key="your-key")gate.register( "my_bot", "ReportBot", "Summarize quarterly business reports", authorized_resources=["/reports/*"], authorized_actions=["read"],) # Authorize before each action — PERMIT | ESCALATE | DENYresult = gate.authorize("read", "/reports/q3.pdf") # Or use the decorator — enforcement is automatic@gate.guard("read", resource_arg="path")def read_document(path: str) -> str: return open(path).read()
One product. Three entry points.
Whether you're a developer trying it out, an engineering team in production, or a compliance team facing a deadline — AgentGate has a path for you.
Developer
Open source. Install in 60 seconds.
Drop into any LangGraph, LangChain, or custom agent pipeline. MIT license. No infrastructure required.
pip install agentgate-pdp
View on GitHubEnterprise
Managed deployment. Full support.
Production-grade deployment with enterprise SLAs, SSO, dedicated onboarding, and direct access to the founding team.
Pilot pricing available
Request a pilotCompliance
EU AI Act ready. Audit-proof.
Full audit trail, human-in-the-loop escalation, and governance controls your auditors can actually review. High-risk obligations take effect August 2026.
Deadline: August 2026
Talk to usGet Early Access
We're onboarding select enterprise pilot teams with limited availability.
Priority given to teams running LangGraph, LangChain, or custom agent frameworks in production with real compliance requirements.
Dedicated onboarding
1:1 setup with the founding team
Pilot pricing
Flexible pricing for early adopters
Direct influence
Shape the roadmap with your use case
We'll reach out within 24 hours to schedule your onboarding call.