agent-field

agentfield

<div align="center"> # AgentField — The AI Backend ### **Build and scale AI agents like APIs. Deploy, observe, and prove.** *AI has outgrown chatbots and prompt orchestrators. Backend agents need backend infrastructure.* [](https://github.com/Agent-Field/agentfield/stargazers) [](LICENSE) [](https://github.com/Agent-Field/agentfield) [](https://github.com/Agent-Field/agentfield/actions/workflows/coverage.yml) [](https://github.com/Agent-Field/agentfield/commits/main) [](https://discord.gg/aBHaXMkpqh) <!-- All outbound agentfield.ai links must use UTM parameters, not direct URLs. See assets/utm-links.csv for the full list — target URLs there are for verification and reference only, always use the UTM version in the README. Update both the README and the CSV when adding or changing links. --> **[Docs](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-docs)** · **[Quick Start](https://agentfield.ai/docs/learn/quickstart?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-quickstart)** · **[Python SDK](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-python-sdk)** · **[Go SDK](https://agentfield.ai/docs/reference/sdks/go?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-go-sdk)** · **[TypeScript SDK](https://agentfield.ai/docs/reference/sdks/typescript?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-typescript-sdk)** · **[REST API](https://agentfield.ai/docs/reference/sdks/rest-api?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-rest-api)** · **[Examples](#built-with-agentfield)** · **[Discord](https://discord.gg/aBHaXMkpqh)** </div> <div align="center"> <a href="https://agentfield.ai/docs/build/intelligence/harness?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-harness-banner"> <img src="assets/harness-banner.png" alt="Now includes Harness Orchestration — multi-turn coding agents with Claude Code, Codex, Gemini CLI, and OpenCode" width="100%" /> </a> </div> AgentField is an open-source control plane that lets you build AI agents callable by any service in your stack - frontends, backends, other agents, cron jobs - just like any other API. You write agent logic in Python, Go, or TypeScript. AgentField turns it into production infrastructure: routing, coordination, memory, async execution, and cryptographic audit trails. Every function becomes a REST endpoint. Every agent gets a cryptographic identity. Every decision is traceable. <div align="center"> https://github.com/user-attachments/assets/9fb7b1cf-26de-4b9b-9ba2-917252cc26ec _{<b>One prompt → a running containerized production ready multi-agent backend.</b> No glue code, start using the agent API!} </div> ## Build production agents with a prompt. **Describe the system in one line. Get a production-ready multi-agent backend.** Works in Claude Code, Codex, Gemini CLI, OpenCode, Aider, Windsurf, and Cursor. ```bash curl -fsSL https://agentfield.ai/install.sh | bash ``` Then in your coding agent, paste any spec with /agentfield : ```text /agentfield Build a claims processor with risk scoring, pattern detection, and human approval for low-confidence decisions. ``` You get a Docker Compose stack wired up end-to-end — the agent, the control plane, and a production ready REST API endpoint you can paste and `curl` into a terminal to try it. [See it in action →](https://agentfield.ai/docs/learn/build-with-claude-code?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-prompt-to-production) ## The DX you get *Best in class Python (or Go / TypeScript) DX. With least intrusive abstraction. No DSL, no YAML, no graph wiring.* ```python from agentfield import Agent, AIConfig from pydantic import BaseModel app = Agent( node_id="claims-processor", version="2.1.0",# Canary deploys, A/B testing, blue-green rollouts ai_config=AIConfig(model="anthropic/claude-sonnet-4-20250514"), ) class Decision(BaseModel): action: str# "approve", "deny", "escalate" confidence: float reasoning: str @app.reasoner(tags=["insurance", "critical"]) async def evaluate_claim(claim: dict) -> dict: # Structured AI judgment - returns typed Pydantic output decision = await app.ai( system="Insurance claims adjuster. Evaluate and decide.", user=f"Claim #{claim['id']}: {claim['description']}", schema=Decision, ) if decision.confidence < 0.85: # Human approval - suspends execution, notifies via webhook, resumes when approved await app.pause( approval_request_id=f"claim-{claim['id']}", approval_request_url=f"https://internal.acme.com/approvals/claim-{claim['id']}", expires_in_hours=48, ) # Route to the next agent - traced through the control plane await app.call("notifier.send_decision", input={ "claim_id": claim["id"], "decision": decision.model_dump(), }) return decision.model_dump() app.run() # This single line exposes: POST /api/v1/execute/claims-processor.evaluate_claim # The agent auto-registers with the control plane, gets a cryptographic identity, and every # execution produces a verifiable, tamper-proof audit trail. ``` > **What you just saw:** `app.ai()` calls an LLM and returns structured output. `app.pause()` suspends for [human approval](https://agentfield.ai/docs/build/execution/human-in-the-loop?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-human-in-the-loop). `app.call()` routes to other agents through the control plane. `app.run()` auto-exposes everything as REST. [Read the full docs →](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-read-full-docs) <details> <summary><b>Prefer to scaffold by hand? (Python / Go / TypeScript / Docker)</b></summary> ```bash af init my-agent --defaults # Scaffold agent cd my-agent && pip install -r requirements.txt af server # Terminal 1 → Dashboard at http://localhost:8080 python main.py # Terminal 2 → Agent auto-registers ``` ```bash # Call your agent curl -X POST http://localhost:8080/api/v1/execute/my-agent.demo_echo \ -H "Content-Type: application/json" \ -d '{"input": {"message": "Hello!"}}' ``` ```bash # Go af init my-agent --defaults --language go && cd my-agent && go run . # TypeScript af init my-agent --defaults --language typescript && cd my-agent && npm install && npm run dev # Docker (control plane only) docker run -p 8080:8080 agentfield/control-plane:latest ``` [Deployment guide →](https://agentfield.ai/docs/reference/deploy?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-deploy) for Docker Compose, Kubernetes, and production setups. </details> ## How AgentField fits in your stack Most agent tools help you **write** agent logic. AgentField is what **runs** it in production — the operating layer that makes agents callable by software, durable across failures, governed by policy, and provable by audit. | | **Frameworks**<br>_{LangChain · CrewAI · PydanticAI · OpenAI Agents SDK} | **Workflow engines**<br>_{Temporal · Airflow} | **Visual builders**<br>_{n8n · Zapier} | **AgentField** | |---|:-:|:-:|:-:|:-:| | Build agent logic (prompts, tools, structured output) | ● | — | — | ● | | Callable production ready REST APIs out-of-box | — | ◐ | ● | ● | | Async + retries + webhooks | — | ● | ◐ | ● | | Memory scopes (global · agent · session · run) | ◐ | — | — | ● | | Service discovery + cross-agent calls | — | — | — | **●** | | Distributed agents | — | — | — | **●** | | Tamper-proof, verifiable audit per execution | — | — | — | **●** | | Harness orchestration (Claude Code · Codex · CLI) | — | — | — | **●** | | Identity and Access Management (IAM) for agents | — | — | — | ● | | Fleet observability (DAGs · metrics · traces) | — | ◐ | — | ● | | Multi-language SDKs (Python · Go · TypeScript) | ◐ | ● | — | ● | _{● full · ◐ partial · — not the focus} **Use a framework when you're proving behavior.** Use AgentField when agents need to be production systems — callable by software, coordinating across services, surviving failures, and governed under audit. [Full comparison & decision guide →](https://agentfield.ai/docs/learn/vs-frameworks?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-vs-frameworks) ## What You Get **Build** - Python, Go, or TypeScript. Every function becomes a REST endpoint. - **[Reasoners & Skills](https://agentfield.ai/docs/build/building-blocks/reasoners?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-reasoners)** - `@app.reasoner()` for AI judgment, `@app.skill()` for deterministic code - **[Structured AI](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-structured-ai)** - `app.ai(schema=MyModel)` → typed Pydantic/Zod output from any LLM - **[Harness](https://agentfield.ai/docs/build/intelligence/harness?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-harness)** - `app.harness("Fix the bug")` dispatches multi-turn tasks to Claude Code, Codex, Gemini CLI, or OpenCode - **[Cross-Agent Calls](https://agentfield.ai/docs/build/coordination/cross-agent-calls?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-cross-agent-calls)** - `app.call("other-agent.func")` routes through the control plane with full tracing - **[Discovery](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-discovery)** - `app.discover(tags=["ml*"])` finds agents and capabilities across the mesh. `tools="discover"` lets LLMs auto-invoke them. - **[Memory](https://agentfield.ai/docs/build/coordination/shared-memory?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-memory)** - `app.memory.set()` / `.get()` / `.search()` - KV + vector search, four scopes, no Redis needed **Run** - Production infrastructure for non-deterministic AI. - **[Async Execution](https://agentfield.ai/docs/build/execution/async?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-async-execution)** - Fire-and-forget with webhooks, SSE streaming, retries. No timeout limits - agents run for hours or days. - **[Human-in-the-Loop](https://agentfield.ai/docs/build/execution/human-in-the-loop?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-human-in-the-loop)** - `app.pause()` suspends execution for human approval. Crash-safe, durable, audited. - **[Canary Deployments](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-canary-deployments)** - Traffic weight routing, A/B testing, blue-green deploys. Roll out agent versions at 5% → 50% → 100%. - **[Observability](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-observability)** - Automatic workflow DAGs, Prometheus `/metrics`, structured logs, execution timeline. **Govern** - IAM for AI agents. Identity, access control, and audit trails - built in. - **[Cryptographic Identity](https://agentfield.ai/docs/build/governance/identity?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-crypto-identity)** - Every agent gets a W3C DID (decentralized identifier) - not a shared API key. Agents authenticate to each other the way services authenticate with mTLS, but with cryptographic signatures that travel with the agent. - **[Verifiable Credentials](https://agentfield.ai/docs/build/governance/credentials?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-verifiable-credentials)** - Tamper-proof receipt for every execution. Offline-verifiable: `af vc verify audit.json`. - **[Policy Enforcement](https://agentfield.ai/docs/build/governance/policy?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-policy-enforcement)** - Tag-based policy gates with cryptographic verification. "Only agents tagged 'finance' can call this" - enforced by infrastructure, not prompts. [See the full production-ready feature set →](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-full-features) <div align="center"> <img src="assets/features-strip.png" alt="90+ Production Features" width="100%" /> </div> <details> <summary><h4 align="center">▼ Click to expand full capabilities</h4></summary> #### AI & LLM | Feature | How | |---|---| | Structured output (Pydantic/Zod) | `app.ai(schema=MyModel)` | | Multi-turn coding agents | `app.harness("task", provider="claude-code")` | | LLM auto-discovers agents and tools | `app.ai(tools="discover")` | | Multimodal (text, image, audio) | `app.ai("Describe", image_url="...")` | | Streaming responses | `app.ai("...", stream=True)` | | 100+ LLMs via LiteLLM | `AIConfig(model="anthropic/claude-sonnet-4-20250514")` | | Temperature, max tokens, format | `app.ai(..., temperature=0.2)` | #### Agent Mesh & Discovery | Feature | How | |---|---| | Cross-agent calls with tracing | `app.call("agent.func", input={...})` | | Discover agents by tag (wildcards) | `app.discover(tags=["ml*"])` | | Discover by health status | `app.discover(health_status="active")` | | Agent routers (namespacing) | `AgentRouter(prefix="billing")` | | Auto context propagation | Workflow, session, actor IDs forwarded | | Parallel agent execution | `asyncio.gather(app.call(...), ...)` | | Auto-registration on startup | Service mesh with zero config | #### Execution Engine | Feature | How | |---|---| | Sync execution (REST) | `POST /api/v1/execute/{agent}.{func}` | | Async (fire-and-forget) | `POST /api/v1/execute/async/{agent}.{func}` | | Webhooks + HMAC-SHA256 signing | `AsyncConfig(webhook_url="...", secret="...")` | | SSE streaming (real-time) | `/api/v1/execute/stream/{id}` | | No timeout limits (hours/days) | Control plane allows unlimited duration | | Execution polling | `GET /api/v1/executions/{id}` | | Batch status checks | `POST /api/v1/executions/batch-status` | | Progress updates mid-execution | Intermediate payloads during long tasks | | Auto retries + exponential backoff | Transparent - control plane handles | | Backpressure + queue depth limits | Fair scheduling, circuit breakers | | Durable queue (PostgreSQL) | Atomic lease-based processing | #### Memory (Distributed State) | Feature | How | |---|---| | Key-value storage | `app.memory.set(key, value)` / `.get(key)` | | Vector search (semantic) | `app.memory.search(embedding, top_k=5)` | | Four scopes | Global, agent, session, run | | Reactive memory events | `@app.memory.on_change("order_*")` | | Metadata filtering | Filter stored values by metadata | | Zero dependencies | Built into control plane - no Redis | #### Human-in-the-Loop | Feature | How | |---|---| | Durable pause/resume | `await app.pause(reason="...")` | | Approval workflows with UI | `approval_request_url` for reviewers | | Configurable timeouts | `expires_in_hours=24` + auto-escalation | | Crash-safe state | Survives agent restarts | #### Canary Deployments & Versioning | Feature | How | |---|---| | Traffic weight routing | 5% → 50% → 100% rollouts | | A/B testing | 50/50 splits with `X-Routed-Version` | | Blue-green deployments | Instant weight switch, zero downtime | | Per-version health tracking | Unhealthy versions auto-removed | | Agent lifecycle states | pending → starting → ready → degraded → offline | #### Identity & Governance | Feature | How | |---|---| | Cryptographic identity per agent | Auto-generated W3C DID + Ed25519 keys | | Verifiable Credentials | Tamper-proof receipt per execution | | Offline VC verification | `af vc verify audit.json` | | Tag-based access policies | ALLOW/DENY rules on caller → target tags | | Cryptographically signed requests | Ed25519 signatures on cross-agent calls | | VC hierarchy (3 tiers) | Platform → Node → Function control | | Agent notes (audit log) | `app.note("Decision", tags=["critical"])` | | Non-repudiation | Cryptographic proof of actions | | Permission request workflows | Auto-created when access denied | #### Observability & Fleet Management | Feature | How | |---|---| | Automatic DAG visualization | Workflow graphs in dashboard | | Prometheus metrics | `/metrics` out of the box | | Structured JSON logging | Automatic from SDK | | Execution timeline | Chronological decision trace | | Health checks (K8s-ready) | `/health`, `/ready` endpoints | | Correlation IDs | `X-Workflow-ID`, `X-Execution-ID` | | Workflow DAG API | `GET /api/v1/workflows/{id}/dag` | | Agent heartbeat monitoring | Auto health status transitions | #### Harness (Multi-turn Coding Agents) | Feature | How | |---|---| | 4 providers | Claude Code, Codex, Gemini CLI, OpenCode | | Schema-constrained output | `schema=ResultModel` (Pydantic/Zod) | | Cost capping | `max_budget_usd=3.0` | | Turn limiting | `max_turns=100` | | Tool access control | `tools=["Read", "Write", "Bash"]` | | Environment injection | `env={"KEY": "value"}` | | System prompt override | `system_prompt="..."` | | Multi-layer output recovery | Cosmetic repair → retry → full retry | #### Connector API (Fleet Management) | Feature | How | |---|---| | Remote agent management | `/connector/reasoners` | | Version traffic control | `/connector/.../weight` | | Bearer token auth | `AGENTFIELD_CONNECTOR_TOKEN` | | Air-gapped deployment | Outbound WebSocket only | #### Developer Experience | Feature | How | |---|---| | CLI scaffolding | `af init my-agent --defaults --language python\|go\|typescript` | | Local dev with dashboard | `af server` → http://localhost:8080 | | Hot reload | `af dev` auto-detects changes | | Auto-REST from decorators | Every `@app.reasoner()` → `POST /api/v1/execute/...` | | Python, Go, TypeScript SDKs | Native patterns per language | | MCP server integration | `af add --mcp --url <server>` | | Config storage API | `POST /api/v1/configs/:key` - database-backed | | Docker + Kubernetes ready | Stateless control plane, horizontal scaling | [Explore all features in detail →](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-explore-features) </details> ## Built With AgentField <table> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/github/swe-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-swe-af-repo"> <img src="assets/examples/autonomous-engineering-team.png" alt="Autonomous Engineering Team" /> </a> <br/> <b>Autonomous Engineering Team</b> <br/> _{One API call spins up PM, architect, coders, QA, reviewers - hundreds of coordinated agents that plan, build, test, and ship.} <br/><br/> <a href="https://agentfield.ai/github/swe-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-swe-af-repo">View project →</a> </td> <td align="center" width="50%"> <a href="https://agentfield.ai/github/deepresearch/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-deepresearch-repo"> <img src="assets/examples/deep-research-engine.png" alt="Deep Research Engine" /> </a> <br/> <b>Deep Research Engine</b> <br/> _{Recursive research backend. Spawns parallel agents, evaluates quality, generates deeper agents, and recurses -10,000+ agents per query.} <br/><br/> <a href="https://agentfield.ai/github/deepresearch/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-deepresearch-repo">View project →</a> </td> </tr> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/github/mongodb/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-mongodb-repo"> <img src="assets/examples/reactive-database-layer.png" alt="Reactive MongoDB Intelligence" /> </a> <br/> <b>Reactive MongoDB Intelligence</b> <br/> _{Atlas Triggers + agent reasoning. Documents arrive raw and leave enriched - risk scores, pattern detection, evidence chains.} <br/><br/> <a href="https://agentfield.ai/github/mongodb/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-mongodb-repo">View project →</a> </td> <td align="center" width="50%"> <a href="https://agentfield.ai/github/sec-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-sec-af-repo"> <img src="assets/examples/ai-security-auditor.png" alt="Autonomous Security Audit" /> </a> <br/> <b>Autonomous Security Audit</b> <br/> _{250 coordinated agents trace every vulnerability source-to-sink and adversarially verify each finding. Confirmed exploits, not pattern flags.} <br/><br/> <a href="https://agentfield.ai/github/sec-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-sec-af-repo">View project →</a> </td> </tr> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/github/cloudsecurity/?utm_source=github-readme&utm_campaign=github-readme&utm_content=cloudsec&utm_id=github-readme-cloudsec-repo"> <img src="assets/examples/cloud-security.png" alt="CloudSecurity AF" /> </a> <br/> <b>CloudSecurity AF</b> <br/> _{AI-native cloud infrastructure security scanner that performs shift-left attack path analysis directly from IaC, prioritizing the most dangerous risk chains before deployment.} <br/><br/> <a href="https://agentfield.ai/github/cloudsecurity/?utm_source=github-readme&utm_campaign=github-readme&utm_content=cloudsec&utm_id=github-readme-cloudsec-repo">View project →</a> </td> <td align="center" width="50%"> <a href="https://agentfield.ai/pr-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-pr-af-repo"> <img src="assets/examples/agentic-pr-reviewer.png" alt="Agentic PR Reviewer" /> </a> <br/> <b>Agentic PR Reviewer</b> <br/> _{Builds a custom review strategy for every PR - spawns parallel reviewer agents with runtime-crafted prompts, adversarially challenges its own findings, and posts evidence-grounded inline comments.} <br/><br/> <a href="https://agentfield.ai/pr-af/?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-pr-af-repo">View project →</a> </td> </tr> </table> [See all examples →](https://www.agentfield.ai/examples?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-see-all-examples) Built something with AgentField? [Submit your project to be featured on the examples page](https://github.com/Agent-Field/agentfield/issues/new?template=community-project.md). ## See It In Action <div align="center"> <img src="assets/UI.png" alt="AgentField Dashboard" width="100%" /> <br/> _{Real-time workflow DAGs · Execution traces · Agent fleet management · Audit trails} </div> ## Architecture <div align="center"> <img src="assets/arch.png" alt="AgentField Architecture" width="100%" /> </div> The control plane is a stateless Go service. Agents connect from anywhere - your laptop, Docker, Kubernetes. They register capabilities, the control plane routes calls between them, tracks execution as DAGs, and enforces policies. [Full architecture docs →](https://agentfield.ai/docs/learn/architecture?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-architecture) ## Learn More The thinking behind AgentField - essays on AI backends, harness orchestration, and the infrastructure production agents actually need. <table> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/blog/what-is-harness-orchestration?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-what-is-harness-orchestration"> <img src="assets/blog/what-is-harness-orchestration.webp" alt="What is harness orchestration?" /> </a> <br/> <b>What is harness orchestration?</b> <br/> _{The atomic unit of intelligence is climbing from the model call to the autonomous harness - and what changes when it does.} <br/><br/> <a href="https://agentfield.ai/blog/what-is-harness-orchestration?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-what-is-harness-orchestration">Read post →</a> </td> <td align="center" width="50%"> <a href="https://agentfield.ai/blog/harness-as-black-box?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-harness-black-box"> <img src="assets/blog/harness-black-box.webp" alt="Part 1: The Black Box" /> </a> <br/> <b>Part 1: The Black Box</b> <br/> _{Treating harnesses like Claude Code and Codex as autonomous, embodied, persistent computational entities.} <br/><br/> <a href="https://agentfield.ai/blog/harness-as-black-box?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-harness-black-box">Read post →</a> </td> </tr> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/blog/harness-as-membrane?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-harness-membrane"> <img src="assets/blog/harness-membrane.webp" alt="Part 2: Engineering the Membrane" /> </a> <br/> <b>Part 2: Engineering the Membrane</b> <br/> _{Shaping the boundary surface of a harness across four engineerable dimensions: workspace, drift, verifier placement, and recovery budget.} <br/><br/> <a href="https://agentfield.ai/blog/harness-as-membrane?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-harness-membrane">Read post →</a> </td> <td align="center" width="50%"> <a href="https://agentfield.ai/blog/ai-backend?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-ai-backend"> <img src="assets/blog/ai-backend.webp" alt="The AI Backend" /> </a> <br/> <b>The AI Backend</b> <br/> _{Our thesis: in five years every serious software company will run an AI backend - a reasoning layer that makes the decisions that used to be hardcoded.} <br/><br/> <a href="https://agentfield.ai/blog/ai-backend?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-ai-backend">Read post →</a> </td> </tr> <tr> <td align="center" width="50%"> <a href="https://agentfield.ai/blog/iam-ai-backends?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-iam"> <img src="assets/blog/iam-ai-backends.png" alt="IAM for AI Backends" /> </a> <br/> <b>IAM for AI Backends</b> <br/> _{Agents need identity, not API keys - how decentralized identifiers and verifiable credentials make agent-to-agent delegation auditable and accountable.} <br/><br/> <a href="https://agentfield.ai/blog/iam-ai-backends?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-blog-iam">Read post →</a> </td> <td align="center" width="50%"> </td> </tr> </table> ### Documentation - **[vs Agent Frameworks](https://agentfield.ai/docs/learn/vs-frameworks?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-vs-frameworks)** - How AgentField compares to LangChain, CrewAI, and workflow engines - **[Full Documentation](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-full-docs)** ## Community <div align="center"> [](https://discord.gg/aBHaXMkpqh) [](https://x.com/agentfield_ai) **[GitHub Issues](https://github.com/Agent-Field/agentfield/issues)** · **[Documentation](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-community-docs)** · **[Examples](https://agentfield.ai/docs/learn/examples?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-community-examples)** </div> ## License [Apache 2.0](LICENSE)