AEGIS Governance
Quantitative governance gate for AI agents. Six gates (risk, profit, novelty, complexity, quality, utility) return PROCEED/PAUSE/HALT/ESCALATE with confidence scores and hash-chained, tamper-evident audit trails. Generates NIST AI RMF and EU AI Act Annex IV artifacts. 10 MCP tools; local stdio and hosted Streamable HTTP with a free tier.
README
AEGIS Governance — MCP Server
Quantitative governance for AI agents and engineering decisions. AEGIS evaluates proposals through six quantitative gates — Risk, Profit, Novelty, Complexity, Quality, Utility — and returns a structured decision (PROCEED / PAUSE / HALT / ESCALATE) with confidence scores, rationale, and a hash-chained audit trail.
Give your agent a decision gate it can call before it acts — and an audit record compliance can actually read (NIST AI RMF, EU AI Act Annex IV).
- Works immediately, no signup: the local server runs in sandbox mode (10 evaluations/day).
- 6 local tools (evaluations, risk checks, health, decision history, usage) — 10 on the hosted server.
- Hosted server with hash-chained audit trails — free Community tier (100 evaluations/month, no credit card).
- Want to see it before connecting? Try the Advisor in your browser — no install, no signup.
Quickstart (local, no account needed)
pip install "aegis-governance[mcp]"
Claude Code
claude mcp add aegis -- aegis-mcp-server
Cursor (.cursor/mcp.json) / Windsurf / any stdio MCP client:
{
"mcpServers": {
"aegis": { "command": "aegis-mcp-server" }
}
}
VS Code (.vscode/mcp.json):
{
"servers": {
"aegis": { "type": "stdio", "command": "aegis-mcp-server" }
}
}
Runs in sandbox mode out of the box. Set AEGIS_API_KEY in the server's
environment (free key)
to unlock decision history, usage reports, and risk checks. Requires Python >= 3.10.
Hosted server (streamable-http, full 10-tool surface)
Get a free API key at portal.undercurrentholdings.com (GitHub/Google sign-in, key provisioned automatically), then:
Claude Code
claude mcp add --transport streamable-http aegis https://mcp.aegis.undercurrentholdings.com/mcp \
--header "Authorization: Bearer YOUR_API_KEY"
Cursor (.cursor/mcp.json) / Windsurf / any streamable-http MCP client:
{
"mcpServers": {
"aegis": {
"type": "streamable-http",
"url": "https://mcp.aegis.undercurrentholdings.com/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
VS Code (.vscode/mcp.json):
{
"servers": {
"aegis": {
"type": "http",
"url": "https://mcp.aegis.undercurrentholdings.com/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
Prefer a local SDK instead of MCP?
The Python SDK has a sandbox mode that works with no account at all (10 evaluations/day):
pip install aegis-governance
from aegis import Aegis
decision = Aegis().evaluate(
proposal_summary="Add Redis caching layer to reduce API latency",
risk_baseline=0.02, risk_proposed=0.05,
novelty_score=0.75, complexity_score=0.8, quality_score=0.9,
)
print(decision.status) # "proceed"
The local stdio MCP server above ships in
aegis-governance>= 1.3.0 via the[mcp]extra.
Tools
| Tool | What it does |
|---|---|
aegis_evaluate_proposal |
Full six-gate evaluation of a proposal; returns PROCEED/PAUSE/HALT/ESCALATE with per-gate scores and rationale |
aegis_quick_risk_check |
Fast risk screen for a proposed change |
aegis_check_thresholds |
Current gate threshold configuration |
aegis_get_scoring_guide |
Domain-specific guidance for deriving gate parameters (e.g. cicd) |
aegis_record_proposal |
Record a proposal for later verification |
aegis_list_proposals |
List recorded proposals |
aegis_verify_proposals |
Verify recorded proposals against outcomes |
aegis_list_decisions |
List past governance decisions |
aegis_get_decision |
Fetch a specific decision with full audit detail |
aegis_crypto_status |
Hash-chain audit integrity status |
Why a governance gate?
AI agents make thousands of decisions with no record of why. AEGIS gives every consequential action a quantitative evaluation and a tamper-evident audit entry — so "the agent decided to deploy" becomes a signed, replayable record with gate scores and rationale.
- Six gates: Risk, Profit, Novelty, Complexity, Quality, Utility — calibrated thresholds, KL-divergence drift detection
- Audit-ready: hash-chained decision log; NIST AI RMF and EU AI Act Annex IV artifact generation
- Five integration surfaces: MCP (this repo), Python SDK, REST API, CLI, GitHub Action
Links
- Docs: aegis.undercurrentholdings.com/docs · MCP tools reference
- Try it in the browser (no install): AEGIS Advisor
- Pricing: portal.undercurrentholdings.com/pricing — free Community tier; paid tiers for teams and regulated environments
- Source distribution: PyPI
aegis-governance(BSL-1.1)
Built by Undercurrent — Agency over agents.
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