Six-month agent engineer roadmap includes Jev-style System-One routing
A six-stage learning plan whose fourth stage uses Jev-style typed decision models to route 95% of traffic to a fast model and escalate 5% to heavy reasoning.
If I had 6 months to become an AI Engineer in the Agentic Era. I’d do this. Stage 1: Context Engineering & Token Economics - Learn: Token math, context window limits, dynamic assembly, context compression, budget allocation. - Practice: Build a dynamic context assembler that strictly budgets tokens across working memory, tool schemas and retrieved docs per request. - Stage 2: Durable Agent Orchestration - Learn: State machines, Temporal, LangGraph, checkpointing, human-in-the-loop (HITL) gates. - Practice: Build a 4-hour agent workflow that survives server crashes, API timeouts and waits 3 days for human approval before resuming. - Stage 3: Tool Use and MCP (Model Context Protocol) - Learn: JSON-RPC, strict Pydantic schemas, tool discovery, scoped credentials, MCP servers/clients. - Practice: Write a raw MCP server from scratch exposing your database and internal APIs without using a wrapper SDK. - Stage 4: System-One vs. System-Two Routing - Learn: Typed decision models (Jev-style), confidence scoring, latency vs. cost tradeoffs, model cascades. - Practice: Build a triage router that sends 95% of traffic to a fast System-One model and escalates only the 5% edge cases to a heavy reasoning model. - Stage 5: Advanced Agentic RAG - Learn: Contextual retrieval, GraphRAG, query rewriting, self-correction, hybrid search, abstention logic. - Practice: Build a RAG agent that critiques its own search results, rewrites ambiguous queries and confidently refuses to answer if grounded context is weak. - Stage 6: 3-Tier Memory Architectures - Learn: Working memory (scratchpad), Episodic (compressed session logs), Semantic (vector DB), eviction policies. - Practice: Build a memory engine that automatically compresses, scores and evicts old context to keep the agent sharp over long sessions. - Stage 7: Trajectory Evals & CI Gates - Learn: Step-level grading, LLM-as-a-judge calibration, golden datasets, regression testing, shadow routing. - Practice: Build a CI pipeline that grades the agent's tool-call sequence and blocks PRs if it deviates from the golden path. - Stage 8: Inter-Agent Security & Guardrails - Learn: Prompt injection defense, PII masking, least-privilege tool scoping, sanitization proxies. - Practice: Build a middleware that swaps PII for UUIDs before the LLM sees it and prevents Agent A's output from injecting Agent B. - Stage 9: Real-Time Multimodal & Voice UX - Learn: Server-Sent Events (SSE), WebSockets, Voice Activity Detection (VAD), interruption handling, optimistic UI. - Practice: Build a voice agent with sub-1s interruption handling that streams tool calls and text simultaneously. - Stage 10: The DPO Data Flywheel - Learn: Preference pairs, LoRA fine-tuning, automated feedback loops, synthetic edge-case generation. - Practice: Automate a pipeline that captures user thumbs-downs, formats them into preference pairs, and triggers a nightly model fine-tune. - Stage 11: FinOps & Graceful Degradation - Learn: Semantic caching, cost kill-switches, fallback chains (Frontier → Mid → Local → Cache). - Practice: Build a middleware that halts an agent loop instantly if projected spend exceeds $0.50 and degrades to a cached response. - Stage 12: Public Teardowns & System Portfolios - Learn: Architecture decision records (ADRs), benchmarking, system diagramming, public writing. - Practice: Publish 3 deep-dives on how you reduced agent latency, cut inference costs or improved tool-call accuracy by 80%. - Wrappers are a commodity. Systems engineering is the moat. The modern AI Engineer builds the deterministic harness around the human brain. Bookmark and Repost!