JamJet vs Microsoft AGT
for AI Agents
Microsoft Agent Governance Toolkit (AGT) launched in April 2026 as an open-source policy library for AI agents. It covers OWASP Agentic Top 10, supports OPA/Cedar/YAML rule DSLs, integrates with most major agent frameworks, and runs in five languages. It is excellent at what it does.
JamJet takes a different shape. It is a safety layer you can wrap around your existing agent framework or use as the runtime your agent runs on — and it brings durable execution, agent memory, signed audit evidence, replay, human approval, and cost caps in the same fabric. This page is an honest comparison: where AGT goes deep, where JamJet goes further, and how to choose.
Last updated 2026-05-08
The quick version
Choose Microsoft AGT when
- You're standardized on Microsoft / Azure and want native Azure App Service deployment
- Policy is the only governance primitive you need today
- Your agent stack spans .NET or Go (AGT covers both natively today)
- You want to author rules in OPA Rego or Cedar specifically
- You don't need durable execution, replay, or hosted memory in the same runtime
Choose JamJet when
- You need policy plus durability, memory, audit evidence, replay, and HITL in one fabric
- You want to wrap your existing framework (LangGraph, CrewAI, Spring AI, OpenAI Agents SDK) without switching it
- You need signed, retention-aware audit exports for EU AI Act / financial / healthcare compliance
- You want agent memory (Engram) governed by the same runtime as the rest of the agent
- You prefer a cloud-neutral safety layer over a Microsoft-anchored one
Three ways to use JamJet
JamJet meets you where your agent already runs. Wrap your existing framework with no rewrite, write directly on the JamJet runtime when you want everything native, or run it as JamJet Cloud for the hosted control plane. Same SDK and patchers across all three modes.
| Mode | What you do | What you get today | Roadmap |
|---|---|---|---|
| Wrap | Keep your existing framework (LangGraph / CrewAI / Spring AI / OpenAI Agents SDK / LangChain4j). Drop in JamJet via the auto-patcher, Spring Boot starter, or SDK adapter. | Policy hierarchy, durable steps, signed audit evidence, HITL nodes, cost caps, Engram memory — applied underneath your existing agent code. | Additional framework patchers (Quarkus, .NET, Go) on the roadmap. |
| Native | Write @task / @DurableAgent / Workflow directly on JamJet. Python or Java SDK. | Everything wrap mode does, plus replay-1.0 cassettes, MCP-native client + server, A2A-native, the eval harness, and a coordinator node for multi-agent routing. No framework overhead. | TypeScript SDK on the roadmap; Go SDK after that. |
| Cloud | Same SDK and patchers, point at JamJet Cloud. | Hosted control plane: dashboards, retention policies, Ed25519-signed audit exports, billing, Slack alerts, production-readiness scorecard. Same OSS runtime underneath — Cloud doesn't fork the codebase. | Hosted Engram (memory) on the roadmap; today, Engram self-hosts. |
AGT is wrap-only, and policy is the primitive. JamJet is wrap + native + cloud, with policy alongside durability, memory, replay, audit evidence, human-in-the-loop nodes, and cost caps in any mode.
Feature comparison
| Capability | Microsoft AGT | JamJet |
|---|---|---|
| Durable execution | Not in scope (stateless policy engine). | Event-sourced + checkpoint snapshots. Rust core + Java native runtime. |
| Agent memory | Not in scope. | Engram — temporal knowledge graph, fact extraction, conflict detection, hybrid retrieval, consolidation, 11 MCP tools. |
| Replay | Not in scope. | Replay-1.0 cassettes + CLI verifier; deterministic re-execution from event log. |
| Audit exports for compliance | Policy decisions logged; no signed export format. | Ed25519-signed evidence packages. PDF / OTLP / Splunk / Datadog renderers. Per-entry retention. |
| Human-in-the-loop | Policy can require approval; not a workflow primitive. | First-class pause/resume/approval nodes, durable across restarts. |
| Cost governance | Not in scope. | Per-workflow and per-agent token/dollar budgets, runtime-enforced — execution halts or branches when the limit is exceeded. |
| Policy engine | Sub-millisecond. OPA Rego + Cedar + YAML rule DSLs. OWASP Agentic Top 10 mapped 10/10. | 4-level hierarchy (global → tenant → workflow → node). Glob-pattern tool blocking, model allowlists, delegation scoping. |
| Identity / token delegation | DID-based identity, behavioral trust scoring. | RFC 8693 OAuth token exchange with scope narrowing. AGT goes deeper on identity primitives (DIDs, trust scoring); JamJet's wedge is delegated-token narrowing. |
| Plugin / tool signing | Ed25519 plugin signing. | Policy hierarchy enforced at runtime (4-level: global → tenant → workflow → node). Signed tool manifests on the roadmap. |
| MCP support | Not native. | Client + server, full spec, 11 Engram tools, MCP Registry listed. |
| A2A protocol | Not native. | Client + server, streaming, failure handling, agent discovery. |
| Framework integrations | LangChain, CrewAI, Google ADK, MS Agent Framework, OpenAI Agents SDK, LangGraph, Haystack, PydanticAI, Dify, LlamaIndex. | LangChain4j, Spring AI, OpenAI Agents SDK (via patcher), CrewAI (wrap mode), LangGraph (wrap mode); roadmap includes Quarkus, .NET. |
| Language SDKs | Python, TypeScript, Rust, Go, .NET. | Python, Java; TypeScript on the roadmap. |
| Deployment | Library — self-host alongside your agent; Azure App Service integration. | OSS self-host (Docker, Kubernetes, JVM); JamJet Cloud (hosted control plane). |
| License | MIT. | Apache 2.0. |
Where Microsoft AGT goes deep
AGT is well-built and well-scoped. If your problem really is “policy decisions for AI-agent tool calls,” AGT is one of the strongest options in the category.
- Sub-millisecond policy decisions
- OPA Rego + Cedar + YAML rule DSL coverage — useful if your team already authors policy in those languages
- 10/10 OWASP Agentic Top 10 coverage out of the box
- Widest framework-integration surface in the policy-library category
- Five-language SDK breadth (Python, TypeScript, Rust, Go, .NET)
- DID-based identity + Ed25519 plugin signing primitives
- Microsoft-backed maintenance and Azure-native deployment
Where JamJet goes further
JamJet's thesis is that policy alone isn't enough for AI agents in production. There are six concerns a policy library can't address — not because AGT got them wrong, but because they can't live in a library. They need to live in the runtime.
Durable execution
Policy that blocks an unsafe call is necessary but not sufficient. When the worker dies mid-tool-call, you need the run to resume — not retry from scratch and not lose the policy decisions you already logged. JamJet's event-sourced runtime survives crashes by replaying the event log; AGT doesn't address this.
Agent memory
A policy library has no opinion about what the agent remembers. Engram is JamJet's memory primitive — temporal knowledge graph, fact extraction, conflict detection, consolidation — governed by the same runtime as the rest of the agent.
Replay
Production agents will be questioned. AGT logs decisions; JamJet replays them. Replay-1.0 cassettes plus the CLI verifier let you re-execute a run deterministically and prove what happened.
Audit exports for compliance
EU AI Act, financial, and healthcare auditors don't want a JSON dump. They want an evidence package. JamJet ships Ed25519-signed exports in PDF, OTLP, Splunk, and Datadog formats with per-entry retention. AGT logs policy decisions; turning those into a compliance artifact is on you.
Human-in-the-loop as a workflow primitive
AGT's policy can require approval. JamJet has approval nodes — durable across restarts, with SLA tracking and bulk-decide UX in the hosted control plane.
Cost caps as a runtime primitive
A reflection loop that won't stop spending is a real failure mode for production agents. JamJet enforces token and dollar caps at both workflow and agent granularity at runtime: exceeding a budget halts the execution or routes it to a configurable fallback node. AGT doesn't address this.
These are not features you can add to a policy library — they need to live in the runtime. JamJet builds them as agent-native abstractions from the ground up; you keep your existing framework on top, or write directly on JamJet, or run it as JamJet Cloud.
Can you use both?
Yes, and it's a legitimate combination. AGT and JamJet operate at different layers — AGT enforces policy decisions at tool-call time; JamJet runs the durable execution + memory + audit + HITL layer underneath.
The clean composition: keep AGT for its policy-rule DSLs and OWASP coverage if your team is already invested in them; add JamJet (in wrap mode) for the rest of the safety surface — durability, memory, signed audit evidence, HITL, cost caps. JamJet's auto-patcher sits between your agent and its tools without replacing AGT.
The capability tradeoff
Microsoft AGT gives you
Sub-millisecond policy decisions in five languages, OPA/Cedar/YAML rule DSLs, OWASP Agentic Top 10 coverage, ten framework integrations, and Azure-native deployment. Policy is the primitive; the rest of the safety surface — durability, memory, replay, audit evidence, HITL, cost caps — is on you.
JamJet gives you
Policy plus durability, memory (Engram), replay, signed audit evidence, HITL nodes, and cost caps in one fabric. Wrap mode for your existing framework, native mode for the full set with no overhead, or JamJet Cloud for the hosted control plane. Today's SDK coverage is Python and Java; the framework-integration surface is narrower than AGT's. If your stack is .NET- or Go-heavy and policy is the only primitive you need, AGT covers more languages out of the box.
Ready to try JamJet?
Start with a 60-second quickstart. Add governance controls as you need them.
Also comparing JamJet with Temporal? See the Temporal comparison.