AI Agent Engineer Roadmap
From agent fundamentals to multi-agent production systems — the deepest agent engineering roadmap in Turkey.
Interactive Roadmap
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About this roadmap
The most comprehensive roadmap for 2025-2026's fastest-growing engineering discipline. **14 sections, 100+ steps, 200+ curated resources.** Full coverage from classic AI agent history (BabyAGI, AutoGPT) to 2026 frontier tech like Claude Code / Operator / MCP. Anthropic's "Building Effective Agents", LangGraph + CrewAI + AutoGen + Pydantic AI frameworks, multi-agent orchestration, computer use, agent eval, production observability — the **end-to-end** discipline of being an agent engineer. Goal: in 6 months, move from "someone who uses tool calling" to **an engineer deploying multi-agent systems to production**.
Who is it for?
Junior/mid software engineers, ML engineers, prompt engineers transitioning to agent engineering. Backend/DevOps experience a plus.
What you'll learn
- Choose the right architecture by distinguishing workflow vs agent
- Apply tool use, function calling, parallel tools, streaming tool calls in production
- Design agentic loop patterns like ReAct, Plan-Execute, Reflexion
- Build short/long-term memory architectures (Mem0, Letta, RAG memory)
- Design multi-agent systems (orchestrator-worker, debate, hierarchical)
- Develop MCP servers and integrate existing servers with clients
- Build GUI automation with browser/computer use agents
- Master LangGraph, CrewAI, AutoGen, Pydantic AI frameworks
- Measure agent quality with trajectory eval, SWE-Bench, GAIA benchmarks
- Monitor production agents with observability (Langfuse, Helicone, OpenTelemetry)
- Layered defense against prompt injection (indirect), capability misuse, hallucination cascade
- Safe autonomous systems with human-in-the-loop, capability gating, audit trail
- Design specialized types: coding agent, research agent, sales agent, voice agent
- Ship long-running agents to production with durable workflows (Temporal, Restate)
- Career path from junior to senior agent engineer + portfolio + open-source contribution
Starting context
Fluent in Python/TypeScript, comfortable with REST API + JSON Schema, 20+ hours of LLM API (OpenAI/Anthropic) experience. Ideally completed the Prompt Engineer roadmap.