The trust layer for autonomous AI agents
AgentGate intercepts every agent action before execution — verifying identity, validating delegation chains, and detecting behavioral drift in real time.
$ python demo.py AgentGate PDP — Trust Authorization Layer ─────────────────────────────────────────[REGISTER] agent_id=langchain_bot_001 purpose="Summarize quarterly reports"[TOKEN] issued: ag-tok-a3f9e2b1...[REQUEST] action=read resource=/confidential/salary_data.txt[SCORING] identity=0.92 delegation=0.85 purpose=0.21 behavioral=0.88[TRUST] composite=0.60 threshold=0.75 (HIGH sensitivity)[DECISION] *** DENY ***[REASON] Purpose alignment score 0.21 is below threshold. Declared purpose: "Summarize quarterly reports" Requested resource: "/confidential/salary_data.txt" Scope mismatch: salary data is outside authorized scope.[AUDIT] logged to agentgate_audit.db[ALERT] notification sent to security team
Your agents have credentials. Do you know what they're doing with them?
Enterprises are deploying autonomous AI agents at scale — but the security infrastructure hasn't kept up. Every agent is a potential attack surface.
OAuth can't detect scope creep
Traditional identity systems grant access once and assume good behavior. They cannot detect when an agent exceeds its delegated scope mid-task.
Delegation chains are invisible
When Agent A delegates to Agent B delegates to Agent C — who authorized the final action? No existing tool answers this.
Behavioral drift goes undetected
An agent's behavior shifts silently over time. By the time you notice, the damage is done.
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%Cryptographic token validation + scope boundary enforcement on every request.
Delegation Chain Integrity
25%Full chain traversal: every ancestor's scope is verified before any action proceeds.
Purpose Alignment
30%Sentence embedding similarity between the agent's declared purpose and the requested action.
Behavioral Anomaly Detection
20%Per-agent velocity baselines with exponential moving average — drift triggers ESCALATE.
Real AgentGate dashboard — live decision feed with trust scores, anomaly flags, and human-in-the-loop escalation
The market context
The regulatory and threat landscape is converging. Enterprises need answers now.
68%
of enterprises cannot distinguish between human and AI agent activity
CSA, March 2026
OWASP Top 10
for Agentic Applications published December 2025 — identity abuse explicitly listed
OWASP, Dec 2025
August 2026
EU AI Act high-risk obligations take effect — enterprises have months, not years
EU AI Act
Microsoft just entered this space. The market is real.
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 actionresult = gate.authorize("read", "/reports/q3.pdf")# result["decision"] -> "PERMIT" | "ESCALATE" | "DENY" # Or use the decorator@gate.guard("read", resource_arg="path")def read_document(path: str) -> str: return open(path).read()
Request Early Access
We're onboarding our first 10 enterprise pilot teams.
Priority given to teams running LangChain or AutoGen in production with real compliance requirements.
Dedicated onboarding
1:1 setup with the founder
Pilot pricing
Free during the pilot program
Direct influence
Shape the roadmap with your use case
We'll review your request and get back to you within 48 hours.