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Autonomous Agent Governance — observe, guard, evaluate, and govern AI agent fleets at scale.
AEGIS-X5
Autonomous Agent Governance Platform
Observe, guard, evaluate, and autonomously govern AI agent fleets — at scale.
From first trace to full autonomous loops, in under 2 minutes.
Quick Start · Modules · CLI · API · Templates · Request a Demo
Organizations deploying AI agents face a governance gap: once agents are live, there is no unified way to monitor their behaviour, enforce safety policies, detect drift, and respond autonomously.
AEGIS-X5 closes that gap with a single SDK that wraps any agent — Claude, GPT, LangChain, CrewAI — and provides real-time governance from day one.
1. Install
pip install aegis-x5
2. Initialize your project
aegis init
3. Add two lines to your agent
from aegis import Aegis
aegis = Aegis() # local mode — no API key needed
@aegis.observe("my-agent")
@aegis.protect("safety-check", level="N2")
def my_agent(prompt: str) -> str:
# your agent logic here
return result
4. See your first metrics
aegis dashboard
# Open http://localhost:4005
That's it. Your agent is now governed — traces, latency, cost, and guard status visible in real time.
| Module | Purpose | Key Features |
|---|---|---|
| Observe | Real-time telemetry | Distributed tracing, token counting, cost calculation, latency metrics |
| Guard | Policy enforcement | PII detection, injection prevention, hallucination checks, N1-N4 severity levels |
| Evaluate | Quality assessment | Faithfulness, relevancy, context precision, golden set testing, drift detection |
| Collect | Feedback ingestion | Structured sources, web collection, confidence scoring, scheduled polling |
| Remember | Memory management | Agent memory, provenance tracking (PROV-O), GDPR/CCPA erasure compliance |
| Predict | ML predictive analytics | Health Score 0-100, drift prediction 48h ahead, cost forecasting, anomaly detection |
| Loops | Autonomous control | Drift auto-correct, guard auto-tune, latency auto-scale, HITL approval gates |
| Mode | Behaviour |
|---|---|
monitor | Observe and alert only — no automated actions |
semi-auto | Auto-correct low-risk; human approval for high-risk |
full-auto | Fully autonomous governance with closed-loop control |
AEGIS-X5 operates in two modes depending on configuration:
| Developer (Local) | Enterprise (Cloud) | |
|---|---|---|
| Setup | Aegis() — zero config | Aegis(workspace="org", api_key="ak_...") |
| Storage | SQLite (~/.aegis/local.db) | PostgreSQL + Redis |
| Dashboard | aegis dashboard (port 4005) | Hosted platform |
| Cost | Free for local use | Commercial license |
| Scaling | Single machine | Multi-tenant, multi-workspace |
aegis init Create aegis.yaml in current project
aegis status Show agents, traces, and stats
aegis dashboard Launch local dashboard (port 4005)
aegis test Run evaluations on golden set
REST API served on port 4000 (Docker) or embedded.
| Method | Endpoint | Description |
|---|---|---|
POST | /api/v1/trace | Record a trace span |
POST | /api/v1/guard/validate | Validate content through guard pipeline |
GET | /api/v1/health | Health check (public) |
GET | /api/v1/agents | List agents with health scores |
GET | /api/v1/predictions | Active predictions and accuracy |
GET | /api/v1/stats | Aggregate trace statistics |
GET | /api/v1/traces | Recent traces |
Authentication via X-API-Key header. See docs/api.md for details.
AEGIS-X5 includes a built-in prediction engine (zero external ML dependencies):
Industry-specific governance profiles that pre-configure validators, thresholds, and evaluation sets.
Built for occupational safety agents in Quebec/Canadian regulatory context:
from aegis.templates import load_template
tpl = load_template("hse")
# tpl.validators, tpl.golden_set, tpl.sources, tpl.regulations
Full platform deployment with PostgreSQL, Redis, API, and Dashboard:
cp .env.example .env
make up
| Service | Port | Description |
|---|---|---|
| API | 4000 | REST API + Swagger docs |
| Dashboard | 4005 | Real-time monitoring UI |
| PostgreSQL | 5432 | Persistent storage |
| Redis | 6379 | Cache + sessions |
Ready-to-run examples in examples/:
claude_agent.py — Claude agent with @aegis.protectlangchain_rag.py — RAG pipeline with observe + evaluatecrewai_team.py — Multi-agent team with guard on each agentopenai_agent.py — OpenAI agent with observefastapi_endpoint.py — API endpoint protected by guardaegis
├── core/ # SDK foundation, config, multi-tenant, tracing
├── observe/ # Telemetry, tokens, cost, latency metrics
├── guard/ # Validator pipeline, PII, injection, hallucination
├── evaluate/ # Metrics, drift detection, eval runner
���── collect/ # Source registry, web collection, scheduler
├── remember/ # Agent memory, provenance, GDPR erasure
├── predict/ # Health score, drift/cost/anomaly prediction
├── loops/ # Autonomous closed loops, orchestrator
├── templates/ # Industry profiles (HSE, ...)
├── dashboard/ # Local web UI
├── api/ # REST API (FastAPI)
├── local/ # SQLite standalone storage
└── cli.py # CLI entry point
AEGIS-X5 is a commercial platform by Preventera.
For enterprise licensing, custom templates, or a guided demo:
© Preventera · GenAISafety · ReadinessX5™
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.3.0 | 9 Apr 2026 | 8 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 20:19 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:cd3b7a38d4d52a0378efa8b37c9520c17c1872ff1ad4cb2a80da64382c869643