AegisGrid

Inspiration

As autonomous AI agents shift from passive chat assistants into active operators capable of issuing tool calls, modifying databases, and triggering real-world transactions, traditional enterprise security boundaries fail. Granting agents direct access to internal APIs creates significant risks of data exfiltration, unauthorized payouts, and prompt-injection-driven actions. Standard Web Application Firewalls and static regex filters cannot evaluate the semantic intent behind complex tool parameters.

We built AegisGrid to solve this fundamental vulnerability: a real-time Zero-Trust AI Gateway and Policy Decision Point (PDP) that intercepts agent actions before they execute on downstream APIs, evaluating them against natural-language enterprise guardrails with human-in-the-loop oversight.

What It Does

AegisGrid is a real-time Zero-Trust AI Gateway & Policy Decision Point (PDP) that intercepts autonomous AI agent tool calls before execution:

Real-Time Policy Enforcement: Evaluates incoming agent actions (such as refunds, bulk data exports, credential access) against natural-language enterprise guardrails using Gemini 3.6 Flash semantic reasoning.
Deterministic Security Verdicts: Assigns numeric risk scores (1 to 10) and renders actionable ALLOW, BLOCK, or ESCALATE decisions.
Human-in-the-Loop Escalations: High-risk or boundary actions route to an administrative Pending Approvals and Escalation Queue for human sign-off before downstream execution occurs.
Cryptographic Audit Ledger: Records tamper-evident SHA-256 audit logs of all intercepted payloads, verdicts, and reviewer identities for regulatory compliance.
Dual-Role RBAC: Enforces strict privilege boundaries between STAFF (draft policies, test payloads in the sandbox) and ADMIN (approve policies, override escalations, manage agent fleet).

How We Built It

Semantic Evaluation Engine: Powered by Google Gemini 3.6 Flash via the google-genai SDK using Pydantic structured outputs (PolicyDecision).
Backend & PDP Gateway: Built with FastAPI / Python providing REST endpoints for payload evaluation, agent fleet management, and audit persistence using an ephemeral-aware SQLite database.
Frontend Application: Developed with React 19, Vite, and Tailwind CSS v4, featuring a Zero-Trust metrics dashboard, natural-language Policy Engine, live Interception Sandbox, and an Escalation Review Queue.

Challenges We Ran Into

Enforcing Deterministic Tool Interception: Getting LLM evaluators to produce consistent, structured outputs without hallucinating or bypassing edge cases. We resolved this by enforcing strict Pydantic schemas via the google-genai SDK, constraining responses to strictly typed ALLOW, BLOCK, or ESCALATE verdicts.
Latency Overhead: Intercepting every agent tool call before execution means gateway latency directly impacts user experience. We minimized evaluation latency by leveraging Gemini 3.6 Flash’s rapid inference and optimizing payload serialization with asynchronous execution.
Managing Complex Escalation Workflows: Designing an atomic human-in-the-loop queue where high-risk agent actions remain paused in a deterministic state until an authenticated operator signs off.

Accomplishments That We're Proud Of

End-to-End Zero-Trust AI Gateway: Intercepts real-time agent tool payloads and applies dynamic, natural-language enterprise guardrails with zero regex brittle points.
Cryptographic Audit Trail: Maintained a tamper-evident SHA-256 audit ledger tracking payload hashes, reviewer IDs, and timestamps for regulatory compliance.
Production-Grade Dashboard: Shipped an intuitive, responsive management interface featuring an interactive Interception Sandbox, policy management studio, and administrative escalation queue with role-based access control (RBAC).

What We Learned

How to effectively implement Pydantic structured outputs with the latest google-genai SDK to guarantee deterministic, machine-actionable security verdicts.
The architectural challenges of applying Zero-Trust principles ("never trust, always verify") to autonomous, non-deterministic agentic systems.
How to design human-in-the-loop workflows that maintain enterprise safety without sacrificing agent autonomy.

What's Next for AegisGrid

Multi-Model Gateway Support: Adding configurable fallback evaluators across different model families.
Pre-Built Enterprise Connectors: Providing drop-in middleware adapters for LangChain, CrewAI, AutoGen, and Model Context Protocol (MCP) servers.
Automated Red-Teaming: Developing an adversarial test suite that automatically generates prompt-injection and privilege-escalation payloads to stress-test enterprise guardrails.