The production infrastructure layer for AI agents. Build, deploy, and scale multi-agent systems with a high-performance Rust data plane, AGNTCY-native interoperability, formal verification, and enterprise-grade governance.

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Rust Data Plane · AGNTCY-Native · 100+ Patterns · 60+ Mesh Services · 18 LLM Providers · Formal Verification · MCP + A2A

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88% of AI pilots never reach production. The bottleneck isn't the models — it's the infrastructure around them. Every team that gets serious about production agents ends up building the same operational stack from scratch: observability, reliability, cost control, security, deployment.

AgentiCraft is that stack. One platform. One command. Any scale.

High-performance Rust data plane. A multi-stage middleware pipeline processes every agent request at microsecond latency — provider routing, privacy classification, cost controls, and reliability enforcement. Policy enforcement happens in the data plane, not in Python.

AGNTCY-native. AgentiCraft is a full-stack implementation of the Linux Foundation's emerging "Internet of Agents" standard (contributed by Cisco) — OASF agent schemas, a federated discovery directory, W3C Verifiable Credentials identity, and SLIM messaging — implemented end-to-end across the Rust data plane and the Python control plane, not wrapped over an external SDK. Agents discover, identify, and coordinate on the standard the industry is consolidating around.

Formal verification. Agent protocols are verified before deployment. If a coordination pattern has a deadlock or a protocol violation, you find it at compile time — not in a 3 AM incident.

One file, any platform. Define your entire multi-agent system in app.yaml — agents, topology, workflows, policies, plugins. The runtime compiles it into infrastructure and deploys it on a laptop, Docker, Kubernetes, or an edge device.

Research-backed reliability. In our fault tolerance experiments, mesh-coordinated agents maintained 100% task completion with zero cascade failures across all tested failure rates. Hub-and-spoke dropped to 82% at 20% failure rate and 72% at 30%. The architecture isn't theoretical — it's tested.

# app.yaml — agents, topology, workflows, policies in one file
name: research-team
agents:
  - id: researcher
    capabilities: [web_search, paper_analysis]
    models: { default: gpt-5-mini, for_analysis: gpt-5.4 }
    memory: { tiers: { hot: { backend: redis } } }
    autonomy: { level: 0.9 }

  - id: analyst
    capabilities: [data_analysis, visualization]

topology:
  connections:
    - { from: researcher, to: analyst, type: delegate }

workflows:
  - id: deep-dive
    pattern: pipeline
    steps:
      - { id: search, agent: researcher }
      - { id: analyze, agent: analyst }

policies:
  budgets: { org: { monthly_usd: 10.0 } }
  guardrails: { pii: { action: mask } }
  sla: { researcher: core, analyst: standard }

craft start --app app.yaml

• 100+ production patterns — reasoning, coordination, workflow, resilience, RAG, safety, planning
• 60+ mesh services with defined service-level objectives across 6 SLA tiers — security, deployment, gateway, observability, and more
• 18 LLM providers with automatic failover — OpenAI, Anthropic, Google, Mistral, Azure OpenAI, Ollama, and 12 more
• MCP + A2A native — open protocols for tool integration and inter-agent communication
• 3 plugin tiers — agent, app, and middleware plugins for extensibility without forking

• Website — platform overview and early access
• Blog — architecture decisions, research findings, technical deep dives
• Foundation Docs — formal verification toolkit
• Email: hello@agenticraft.ai