Agent Governance Comparison — Agent-Aegis vs Microsoft AGT
Description: Honest comparison: Agent-Aegis (1 dep, 24 papers, 2 lines) vs Microsoft AGT (7 packages, 5 languages, enterprise). Choose the right tool.

# Agent Governance: Choose Your Tool

Agent-Aegis vs Microsoft Agent Governance Toolkit — an honest comparison

1
Agent-Aegis dependency
(PyYAML only)
2
Lines to get started
(vs 9 lines)
24
Academic papers
implemented
1
pip install
(vs 7 packages)

## Different tools for different needs

MS AGT is an enterprise governance platform — 7 packages, 5 languages, SRE, sandboxing, marketplace.

Agent-Aegis is a lightweight research-grade library — 1 dependency (PyYAML), 24 academic papers, one pip install.

Think SQLite vs PostgreSQL. Both are excellent. Pick the one that fits your needs.

## When to Choose What

### Agent-Aegis

pip install agent-aegis — done.

- You want zero external dependencies
- You need 24 research paper implementations
- You're building a Python-only project
- You want governance running in 2 lines of code
- You're a researcher or student
- You need embedded, in-process governance
- You don't want Azure or enterprise infra

### Microsoft AGT

Enterprise agent governance platform.

- You need Python + TypeScript + Rust + Go + .NET
- You need SRE features (SLOs, chaos engineering)
- You need execution ring sandboxing
- You need 12+ framework adapters
- You need agent marketplace / plugin lifecycle
- You need RL training governance
- You're building enterprise-scale multi-agent systems

## Getting Started

Side-by-side: what it takes to add governance to your agent.

Agent-Aegis

    import aegis

    aegis.auto_instrument()

2 lines · 1 dependency (PyYAML) · works immediately
Microsoft AGT

    from agent_os import PolicyEngine, CapabilityModel

    capabilities = CapabilityModel(
        allowed_tools=["web_search", "file_read"],
        denied_tools=["file_write", "shell_exec"],
        max_tokens_per_call=4096
    )
    engine = PolicyEngine(capabilities=capabilities)
    decision = engine.evaluate(
        agent_id="researcher-1",
        action="tool_call",
        tool="web_search"
    )

9+ lines · requires pydantic + rich · configure capabilities first

## Feature Matrix

Capabilities of each framework. Sources linked in methodology.

Feature | Agent-Aegis | MS AGT
--- | --- | ---
Policy engine | Yes | Yes
Built-in guardrails (injection, PII) | Yes (85+ patterns) | No (use Azure AI Content Safety)
Verifiable audit (Merkle proofs) | Yes (O(log n) proofs) | Hash-chain only (JSONL)
Academic paper implementations | 24 papers | 0 (industry best practices)
Minimal dependencies | PyYAML only | pydantic + rich required
OWASP Agentic Top 10 | Partial | 10/10
Multi-language SDKs | Python only | Python, TS, Rust, Go, .NET
Execution sandboxing | No | 4-ring isolation model
SRE (SLOs, chaos, circuit breakers) | No | Yes
Framework adapters | 7 (LangChain, CrewAI, OpenAI, etc.) | 12+ (AutoGen, Semantic Kernel, ADK, etc.)
Temporal monitoring (6 patterns) | Yes | No
MCP security (manifest, ETDI, scanning) | Yes (3 modules) | MCP gateway only
Trust calibration (contextual bandit) | Yes | No
Identity binding (BAID) | Yes | DID-based identity
RAG poisoning detection | Yes (6 methods) | No
Tool poisoning graph analysis | Yes | No
Agent marketplace | No | Yes
RL training governance | No | Yes

## Performance Notes

## Fair warning

These benchmarks were run on different hardware (Agent-Aegis: Apple Silicon; MS AGT: Windows AMD64). They are not directly comparable. Use for order-of-magnitude context only.

Metric | Agent-Aegis | MS AGT | Note
--- | --- | --- | ---
Single rule policy eval (p50) | 3.5 µs | 11 µs | Different hardware
Policy eval ops/sec | 249K | 84K | Different hardware
Circuit breaker check | 1.1 µs | 0.5 µs | MS AGT faster
Audit entry write | 10.4 µs | 2.0 µs | Merkle tree vs JSONL append
Merkle inclusion proof | 2.7 µs | N/A | Agent-Aegis only
Install size | ~2 MB | ~53 MB (full) | With all packages
External dependencies | 1 (PyYAML) | pydantic + rich + more | Core package deps

## Research Coverage: 24 Academic Papers

Agent-Aegis implements peer-reviewed research as pure Python modules. Each paper is a working implementation — not a citation. Zero external dependencies. Thread-safe. Sub-millisecond. No other governance framework offers this.

MCP Manifest Signing mcp_manifest.py
ETDI Tool Version Pinning etdi.py
MCP Vulnerability Scanning mcp_vuln_scanner.py
MCP Threat Intelligence mcp_threat_intel.py
Trust Scoring trust_score.py
Autonomy Levels (L0–L4) autonomy_level.py
Trust Calibration (LinUCB) trust_calibration.py
Threat Taxonomy (OWASP) threat_taxonomy.py
Tool Poisoning Detection tool_poisoning_graph.py
Temporal Monitoring temporal_monitor.py
RAG Poisoning Guard rag_guard.py
Multi-Agent Anomaly Detection mas_monitor.py
Action Reversibility Scoring reversibility.py
Sandbox Policy Engine sandbox_policy.py
Hash-Chained Execution Traces exec_trace.py
Hazard Classification hazard_classifier.py
Zero-Trust Agent Verification zero_trust_agent.py
Identity Binding (BAID) identity_binding.py
Audit Lifecycle & Compliance audit_lifecycle.py
Data Isolation Boundaries data_isolation.py

## Market Positioning

| Action Governance | Content Guardrails | Verifiable Audit | Research Depth
--- | --- | --- | --- | ---
MS AGT | Yes | No (use Azure) | Hash chain | Industry practices
Guardrails AI | No | Yes | No | No
NeMo Guardrails | Partial | Yes | No | No
Agent-Aegis | Yes | Yes (built-in) | Merkle proofs | 24 papers

### Methodology & Sources

- Agent-Aegis benchmarks: `tests/bench_paper_modules.py`, Python 3.12, Apple Silicon M-series. Source: github.com/Acacian/aegis
- MS AGT benchmarks: BENCHMARKS.md, v2.1.0, Windows 11 AMD64, 10,000 iterations
- MS AGT feature claims: README.md, OWASP-COMPLIANCE.md
- MS AGT dependencies: agent-os/pyproject.toml (pydantic ≥ 2.4, rich ≥ 13)
- Benchmarks run on different hardware — performance numbers are not directly comparable
- Feature presence verified from public GitHub source code as of April 2026

Corrections welcome. Open an issue if anything is inaccurate.

`pip install agent-aegis`

24 academic papers. 1 dependency. Two lines of code.

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