AgentField.ai

🚀 AgentField is live - the open-source AI backend for autonomous software. Agents are moving beyond chat into systems where they touch money, data, and decisions. The traditional backend stack (OAuth for humans, API keys for static services, DAGs for brittle flows) breaks there. AgentField gives agents cryptographic identity (DIDs), delegated trust (VCs), long-running execution, async orchestration, and verifiable audit receipts - so you can run agents like micro-services in production, with policy-as-code and full observability.

Stop building AI agent workflows like it's 2023. 🙅 The left side of this image is what most agent code looks like today: → Define every node manually → Wire edges between them one by one → Add conditional routing logic → Compile the graph → And you STILL don't have identity, memory, discovery, or deployment The right side is AgentField: → Write a Python function → It becomes a distributed service automatically → Agents discover and call each other by name (Agent mesh!) → Structured output through Pydantic, not prompt hacks → Identity, memory, deployment - all built in One function on the right replaces 30+ lines of graph wiring on the left. 🪄 And it scales to 1000's agents without changing a single line. No DAGs. No YAML. No workflow DSLs. SDKs in python, tsx and go. 🎯 Try it out - lnkd.in/gj3GG3mH Open source. Apache 2.0.

💡 AgentField is now a member of the Agentic AI Foundation (AAIF). 146 organizations collaborating on open standards and protocols for agentic AI - interoperability, identity, governance, and production-ready infrastructure. We've been building toward this from day one. Agents in production need open, verifiable foundations - not proprietary silos. The AAIF is the right place for that work. Glad to be part of it alongside Anthropic, OpenAI, JPMorganChase , American Express, Red Hat, ServiceNow and many others. 💫 → aaif.io/members/

AI agent governance isn't a new problem. You solved it 10 years ago for microservices — mTLS, Service mesh, Centralized auth, Immutable logs. Now you're deploying AI agents that approve invoices , and trigger workflows — and the audit trail is... a JSON file in S3? We've been here before. The answer is the same: infrastructure-grade identity and governance. AgentField gives every AI agent: 🔐 Cryptographic identity (not a shared API key) 🛡️Tamper-proof audit trail (not editable logs) 🔗Delegation chains (not "it was authorized...probably") ✨Ed25519 signatures on every action Your compliance team will thank you. Your future self will thank you more. Open source: lnkd.in/ecrGsq9y

Yes, your agent demo works. Now try running it 8.2 million times per second. 🚀 If intelligence is moving into your backend, the runtime matters. AgentField delivers infrastructure grade throughput for autonomous software. Lightweight control plane built for scale. ⚡️ Checkout our latest benchmarks - lnkd.in/g-MJnxUw

Building production AI agents shouldn't feel like building infrastructure from scratch. 🤖 But right now, most teams are writing: → Queue setup code → Worker pool management → Retry handlers → Webhook delivery + signatures → Monitoring and alerting → All the Terraform to glue it together ~15 files. ~2000 lines. ~3 months. With AgentField: → 1 function → 1 API call → ~3 minutes The infrastructure is the product. You just write your agent logic. 🛠️ Open source: lnkd.in/ecrGsq9y

Zero trust Agent-to-Agent Authentication ? If you're building multi-agent systems and you're not thinking about this, you should be. When Agent A calls Agent B: - How does B verify A's identity? - How do you audit who called whom? - How do you revoke access if something goes wrong? AgentField: cryptographic identity for every agent. DID-based verification. Zero-trust by default. Let Identity and auth be part of your infrastructure. And the best developer experience is cherry on top 🍒 ( yes its that easy! ) Open source: lnkd.in/ecrGsq9y

Hot take 🔥: Most AI agent frameworks think everything has to live in one cozy box. What if your agents could run on-prem - and still just… work together seamlessly? No custom networking mess No message queues to babysit No serialization nightmares Just: Agent A calls Agent B. The AI Backend magic handles the rest Haven't checked out Agentfield yet? 👀 → agentfield.ai Open-Source • Apache 2.0 🚀

The next infrastructure layer isn't about smarter prompts. It's about agents that deploy like microservices, discover each other, and prove what they did. Singapore → Feb 7th. Let's build the next layer together. 🇸🇬🚀

What happens when deep research enters your AI backend? Most research tools are built for humans to read. When the machine is the consumer, your entire architecture changes, you optimize for computation, instead of comprehension. Today we're releasing AF Deep Research, a deep research unlike any other, built for machine-native research. 🚀 What makes this architecture different: • Guided exploration at scale: 1000's of agents deciding what to investigate next, within defined boundaries • Higher-order analysis: surfaces cross-source correlations and causal relationships no single document reveals • Structured output for machines: typed entities, directed relationships, evidence chains with citations. Data systems can reason over, not just read • Full infrastructure control: open-source models, no third-party constraints, deploy inside your own environment • Unlimited parallel scaling, you control how many agents run. No rate limits. No waiting. Here’s where it gets interesting. A few examples of what's possible: • M&A due diligence: agents parallelizing across patents, supplier networks, regulatory exposure, key person risk. Coordinating to find what's missing from the data room. Output flows directly into valuation models. • Clinical trial design: agents researching competing protocols, FDA feedback letters, biomarker validation literature. Exploring novel endpoints as they surface. Structured output plugs into trial design software. • Supply chain risk: agents recursively mapping Tier-N suppliers to surface hidden concentration risks in suppliers-of-suppliers you've never heard of. Risk scores feed straight into SAP Ariba or Resilinc. These are just a few. We are more excited to see what others build on top of it. AF Deep Research currently supports OpenRouter for AI Models, Jina AI, Tavily, Firecrawl and SerpApi for scraping, AgentField.ai is the control layer that turns raw data into structured intelligence. 2 lines of code to host and try it. Apache 2.0. Self-host it. Bring your own models. AgentField: lnkd.in/ecrGsq9y AF Deep Research: lnkd.in/g9XYVSh2 What would you build with this massive scale of research as infrastructure? 👇

If you’re building with agents - copilots, internal automations, agentic apps, AI-driven ops - the constraints you’re hitting (auth, long-running tasks, orchestration chaos, missing audit) are symptoms of the same root issue: The stack wasn’t designed for autonomous software. AgentField is that missing backend layer: run agents like services, with proof, not logs. Read the announcement: siliconangle.com/2025/12/10/age…

Why this matters: as autonomous software becomes real, we need an AI-native control plane where agents can: • carry identity + authority across hops • run long, branching, multi-agent workflows (hours/days) • generate tamper-proof receipts for every action • operate safely inside production systems behind guardrails and policy That’s what we built with AgentField.

🚀 AgentField is live - the open-source AI backend for autonomous software. Agents are moving beyond chat into systems where they touch money, data, and decisions. The traditional backend stack (OAuth for humans, API keys for static services, DAGs for brittle flows) breaks there. AgentField gives agents cryptographic identity (DIDs), delegated trust (VCs), long-running execution, async orchestration, and verifiable audit receipts - so you can run agents like micro-services in production, with policy-as-code and full observability.