Open-source · MIT · v0.1.0

The runtime safety layer for AI agents.

Every tool call — classified, gated, audited. Zero code change.

Same code either way. Watch the 90-second tour →  ·  Compare the two paths

01 · Pre-deploy scan
aegis scan ./acme-pay-agent
  • 87 files scanned · Python + TS 1.8s
  • Framework LangGraph 0.2.4 · 3 agents 0.3s
  • 14 tools $ +10 0.7s
  • Risk 6 3 5 0.4s
  • Pack PCI-DSS Travel Rule 0.9s
02 · Plain English → Policy

Describe the rule

Block USDC > $10,000 to non-allowlisted wallets. Require 2 of finance-ops.

compiled · 14 ms · DSL v2.4
rule: "stablecoin-egress-2of2"
when:
  - tool.name == "circle_usdc_transfer"
  - amount > 10_000_00
  - wallet NOT IN treasury.allowlist
require:
  approvers: 2
  scope:    "finance-ops"
action: ESCALATE
03 · Runtime · <50 ms

Live · acme-pay · refund-agent

stripe.refund · $47.00 285 ms ALLOW
coinbase_prime.deposit · 3,200 USDC 412 ms ALLOW
$ circle_usdc.transfer · 24,500 USDC0x7f31…aE92 12 ms ESCALATE

stablecoin-egress-2of2 · wallet not on treasury allowlist · needs 2 approvers

04 · Cryptographic audit

Merkle log · acme-pay · 14,829 events / 24h

root sha256:a3f2…b819
L 7f12…4ab9
R d8c3…0e74
Witness cosignature · witness.aegistraces.com
PCI-DSS · Req 10 SOC 2 · CC8.1 FATF · Travel Rule
  1. 01Scan
  2. 02Policy
  3. 03Block
  4. 04Audit

Compatible with the stacks teams already ship

  • Anthropic
  • OpenAI
  • Stripe
  • Coinbase
  • LangChain
  • Mistral
  • Hugging Face
  • Visa
  • Vercel
  • Google Gemini
  • Circle
  • Brex
  • Mastercard
  • Snowflake
  • Databricks
  • Cloudflare

What you see

Every tool call, in one view.

One dashboard surfaces every agent decision, every block, every anomaly — across every workflow you ship.

Cockpit — overview dashboard with 24h activity curve, branded agent feed, and recent traces

What it does

From pre-deploy scan → runtime block → forensic audit.

Pre-deploy scan

Read your repo before it ships.

Tree-sitter AST across Python / JS / TS. Every tool, every credential, mapped — and a starter policy proposed.

aegis scan ./acme-agent
  • Analyzed 24 files (Python + TS) 1.4s
  • Detected framework LangGraph 0.2.4 0.3s
  • Found 7 tools · 3 HIGH-risk 0.6s
  • Mapped workflow · 3 agents, 12 edges 0.4s
  • Proposing starter policy bundle… 0.8s

Plain English → Policy

Describe what to block. We write the rule.

One sentence in, grammar-constrained DSL out. Auditable, reversible, version-controlled.

Describe the rule

Block emails to personal addresses during checkout flow. Allow [email protected] but flag anything to gmail, outlook, or icloud.

↓ ✨ Generate

rule: "block-personal-email-in-checkout"
when:
  - tool.name == "send_email"
  - context.workflow == "checkout"
recipient:
  deny: ["@gmail.com", "@outlook.com"]
  allow: ["@acme.io"]
action: BLOCK

Runtime block

Every tool call, classified in < 50ms.

Allow, escalate, block — every tool call decided before it leaves your network. Sub-50 ms inline.

Cockpit — real-time agent activity feed

Forensic audit

Violations grouped by policy, by risk.

Every block in a Merkle-chained log. Filter by risk, group by policy, export to your auditor.

Cockpit — violations grouped by policy with risk levels

Agent registry

Know which agents are alive — and who owns them.

Status, owner, scope, key rotation — one place to suspend a misbehaving agent or grant a new scope.

Cockpit — agent registry with status, owner, scope, last-seen brand

Coverage

Know what's protected — and what's still bare.

Per-agent coverage report — which tools are policy-gated, which categories only audit. No mystery gaps.

Cockpit — policy coverage report by agent and tool category

Beyond the call

Tainted memory. Cross-agent leaks. PII without a prompt.

Tainted recall, undeclared agent-to-agent leaks, PII that appears without ever being in the prompt. A distinct layer.

Cockpit — Memory & Cross-Agent layer with tainted recall, agent crossings, and pre-instruction PII

5-minute integration

Two env vars. No SDK rewrite.

Before
import openai
client = openai.OpenAI(
  api_key="sk-xxxx"
)
After (env only)
OPENAI_BASE_URL=https://gateway.aegistraces.com/openai/v1
AEGIS_API_KEY=aeg_xxx

# code unchanged

vs. the category

What others don't ship.

CapabilityAEGISOthers
Cryptographic audit (Merkle + witness)RFC 6962, built-innone
Sequence-aware anomalyn-gram LM, per-agentsingle-call only
Multi-agent collusionburst / relay / cyclesingle-agent only
Workflow → per-node policy5 frameworksnone
Counterfactual explainerverified by re-validationpartial
AST scan rulestree-sitter + YAMLregex only
GenAI OTel semconvfullproprietary
SCIM + SAML + OIDCall threeone or the other
Policy effectiveness scoringP/R/F1 + retire signalnone
LicenseMITclosed

From the field

What builders and researchers say.

Yue Zhao
Yue Zhao @yuezhao_research

Assistant Professor, USC · AI Risk Audit & Control

@AEGIS is the runtime control layer the agent ecosystem has been missing. The architecture is clean, the cryptographic audit is real, and the DSL is the right primitive.
Daniel Park
Daniel Park @danielparkai

Head of AI · healthcare SaaS

HIPAA review used to take six months. With @AEGIS we got the evidence pack in two weeks and the auditor signed off without a follow-up call.
Maya Chen
Maya Chen @itsmayachen

CTO · payments infra

Our refund agent shipped to production the day after we wired @AEGIS in. Two reviewers, three policies, ten minutes. The audit log alone saved us a six-week SOC 2 cycle.
Marcus Webb
Marcus Webb @marcuswebb

CISO · neobank

We tried to write our own policy DSL twice and shipped neither. @AEGIS gave us grammar-constrained NL-to-DSL the same week we integrated. Three policies in production by Friday.
Priya Iyer
Priya Iyer @pricodes

Staff Engineer · healthcare AI

The Memory & Cross-Agent layer in @AEGIS caught two undeclared crossings on day one — neither was in our threat model. We standardized on it for all agent rollouts.
Sarah Kim
Sarah Kim @sarahbuildsai

Founder · agent observability · YC W26

The Merkle audit log is a real moat. Every other guard product I evaluated stores decisions in plain Postgres. @AEGIS is the only one I'd hand to an auditor.
Tom Reeves
Tom Reeves @tomreeves_eth

Head of DevRel · Web3 ops

Stablecoin transfers used to require a human on every $10k+ wire. With @AEGIS the policy enforces 2-of-N approval automatically. Our ops team got their evenings back.