A curated collection of resources, tools, practices, case studies, and opportunities for Forward Deployed Engineers.
An open-source Digital Worker platform for reliable execution, continuous co-evolution, and building Enterprise AI assets.
Engineering deterministic, production-grade systems around non-deterministic LLMs — FSM, durable execution, retries, DAGs, agent runtimes, model routing, edge inference, RAG, memory, multi-agent orchestration, security, and observability. 14 runnable proof-of-concept phases.
The Applied AI and FDE Guide: fieldwork, value engineering, and operations for agents that work beyond the demo.
A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline.
Source-backed, production-first FDE interview fieldbook with cases, scorecards, and current role research.
Universal autonomous agent framework with ReAct loop, multi-provider LLM routing, reasoning graph, and MCP integration, domain-agnostic for building specialized AI agents.
Stop overpaying to run your agents. Kalibr routes every request to lower-cost model and tool paths without degrading performance.
Open source software for machine learning production monitoring : maintain control over production models, detect bias, explain your results.
The open-source safety layer for AI agents — block unsafe tool calls, require approval, enforce budgets, audit, replay.
A production GenAI architecture guide for Forward Deployed Engineer (FDE) and Applied AI interviews—RAG, agents, evals, security, and operations.
Production-grade architecture patterns, decision frameworks, and best practices for building reliable AI agents. Framework-agnostic reference for engineers.
🚀 Build AI Agent Teams as Production-Ready APIs. Orchestrate CrewAI agents with FastAPI for enterprise-grade AI services. Leverage Groq's lightning-fast LLMs to deploy collaborative AI workflows at scale.
Production operations framework for AI-powered SaaS. The architectural patterns, failure modes, and operational playbooks that determine whether your AI systems scale profitably or fail expensively.
Production-ready agentic AI framework. High-performance, lightweight, simple. Built-in safety, memory, and 4 reasoning patterns. Ships to production fast.
Engineering patterns for taking AI agents to production — deployment, tools, memory, long-running work, human oversight, and observability at scale. From Microsoft Build 2026.
AgentOps control plane for AI agent execution. Rust policy plane, hash-chained audit, deny-by-default tool allowlists and budget enforcement, eval gating in CI. Single maintainer, moving fast, read the README before you deploy it. Docs: https://sattyamjjain.github.io/ferrumdeck/
40x faster AI inference: ONNX to TensorRT optimization with FP16/INT8 quantization, multi-GPU support, and deployment
AxonFlow governance for OpenClaw agents — block dangerous tools, govern MCP access, and keep audit trails for production agent workflows
Andrej Karpathy's 2026 AI Systems Engineering Roadmap — 7 production-grade projects covering Context Engineering, Tool Design, Agent Orchestration, Eval Discipline, Reliability Engineering, MCP Servers & Harness Mindset. From PR review agents to autonomous DevOps incident response.
To associate your repository with the production-ai topic, visit your repo's landing page and select "manage topics."