SkillHack › Career guides › Agent Engineer
How to become an Agent Engineer
Multi-agent architectures, orchestration, tool design, and agent reliability. Build agent systems that plan, act, and recover on their own.
- 5phases in the roadmap
- 18topics to work through
- 103graded practice questions
- Freeno payment, ever
The Agent Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1Agent engineering programming foundations
Python mastery
Agents are orchestration code, and in Python that means fluency in async/await, type hints, decorators, and data modelling with dataclasses or Pydantic.
Development environment
Before shipping an agent you need the workshop: Git for versioning, Docker for reproducible environments, the Linux command line, virtual environments, and safe handling of secrets through…
APIs and HTTP
Agents are, at bottom, API orchestrators - they reach models and tools over HTTP.
Phase 2LLM and AI fundamentals
How LLMs work
You cannot debug what you do not understand.
Prompt engineering
Prompts are the agent's source code.
LLM APIs
Everything an agent does runs through a model API - message roles, streaming, function calling, and tool use.
Phase 3Agent core concepts
The agent loop
The core agent pattern interleaves reasoning and action: perceive, reason, act, observe, repeat.
Tool use and function calling
Tools are the agent's hands.
Memory systems
Context windows forget; products cannot.
Planning and reasoning
Big, ambiguous requests become tractable when broken into steps an agent can execute and check.
Phase 4Agent engineering multi-agent systems
Agent architectures
Multi-agent systems have an org chart: orchestrator-worker, supervisor, peer-to-peer, and hierarchical patterns each trade coordination cost against capability.
Designing an agent swarm
Before the org chart comes the harder question: does this work need an agent at all?
Agent communication
Agents that work together must exchange information reliably - message passing, shared state, queues, explicit handoffs, and increasingly the Model Context Protocol (MCP).
Agent frameworks
Frameworks like LangGraph, CrewAI, and AutoGen encode agent patterns you would otherwise build by hand.
Phase 5Agent engineering production
Systems architecture and contracts
The agent is one layer of a stack (surface, orchestration, tools, model gateway, observability, guardrails) and most design mistakes are a requirement implemented at the wrong level of it.
Safety and guardrails
Agents with real tools cause real damage when unsafe.
Observability
You cannot improve what you cannot see.
Deployment and infrastructure
Production agents need what production services need: async workers and job queues for long-running work, persistent state, tenant isolation, and CI/CD.
Reading for this path
The primary sources behind the topics above, all free to read.
- Python documentation
- Real Python: async IO
- Docker: get started
- MIT: The Missing Semester
- MDN: HTTP overview
- FastAPI documentation
- Karpathy: Neural Networks Zero to Hero
- 3Blue1Brown: neural networks
- Anthropic: prompt engineering
- OpenAI Cookbook
- Anthropic: Messages API
- OpenAI: API reference
- ReAct (Yao et al.)
- Anthropic: building effective agents
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
AI Alignment Researcher
Training methods, oversight, and alignment evaluations. Make advanced AI pursue the goals we intend.
AI Automation Engineer
Agents, tool integration, and workflow orchestration. Turn multi-step business processes into automated flows.
AI Data Engineer
Ingestion pipelines, embeddings, vector stores, and data quality for training and retrieval. Feed AI systems clean, fresh, well-shaped data.
AI Developer Advocate
Docs, sample apps, talks, and community feedback loops. Help developers build with AI and carry their voice back to the product.
AI Engineer
Prompt and context design, tools, retrieval, orchestration, evaluation, and structured output. Build reliable agent apps end to end.
AI Evals Engineer
Benchmark design, LLM-as-judge, regression suites, and quality metrics. Measure what models actually do before and after every change.
Start the Agent Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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