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@@ -39,20 +39,27 @@ AgentField is where we build that stack in the open.
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## What lives here
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## What we've shipped
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Production-grade autonomous agents. Open source. Apache 2.0. Each independently deployable.
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This org is the home for:
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|| What it does | Scale |
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|---|---|---|
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|**[SWE-AF](https://github.com/Agent-Field/af-swe-claude)**| Autonomous engineering team — one API call ships planned, coded, tested, reviewed code | 400-500+ agents/build |
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|**[SEC-AF](https://github.com/Agent-Field/sec-af)**| Security auditor that proves exploitability, not just flags patterns | 200-300 agents/audit |
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|**[Contract-AF](https://github.com/Agent-Field/contract-af)**| Legal contract risk analyzer with adversarial verification and negotiation playbooks | 20-100+ agents/contract |
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|**[AF Deep Research](https://github.com/Agent-Field/af-deep-research)**| Autonomous research backend with self-correcting loops | 10,000+ agents/query |
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|**[Reactive Atlas](https://github.com/Agent-Field/af-reactive-atlas-mongodb)**| Turn any MongoDB collection into an AI intelligence layer | 3-14 agents/document |
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-**`agentfield`**
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The open source control plane that runs AI agents like microservices, with queues, async webhooks, discovery, identity and audit in one binary.
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All run on [AgentField](https://github.com/Agent-Field/agentfield), the open source control plane.
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-**SDKs and examples**
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Thin SDKs that let you write plain Python (and other languages over time) while the control plane handles the hard parts: long-running work, streaming notes, multi-agent calls, verifiable credentials.
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---
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-**Specs, playbooks and experiments**
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Reference designs for autonomous backends, IAM patterns for agents, and example workflows that show what a real agent economy looks like in production.
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## What lives here
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If you want to understand where agent infrastructure is going, watching this org will tell you more than any single blog post.
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-**`agentfield`** — The open source control plane: queues, async webhooks, discovery, identity and audit in one binary.
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-**SDKs** — Thin Python SDK (more languages coming) so you write plain code while the control plane handles the hard parts.
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-**Specs and playbooks** — Reference designs for autonomous backends, IAM patterns for agents, and production workflow examples.
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- Watching this org is an easy way to track how the Internet of Agents stack is actually evolving.
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2.**As a lab**
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-Use the examples to prototype serious use cases: refunds, treasury flows, claim handling, compliance checks, research workflows.
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-Run [SWE-AF](https://github.com/Agent-Field/af-swe-claude), [SEC-AF](https://github.com/Agent-Field/sec-af), [Contract-AF](https://github.com/Agent-Field/contract-af), [AF Deep Research](https://github.com/Agent-Field/af-deep-research), or [Reactive Atlas](https://github.com/Agent-Field/af-reactive-atlas-mongodb) against your own projects.
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- Stress test the control plane with the kind of multi-agent fan-out you cannot safely run through a single monolith.
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