If you're moving from "writing prompts" to "building systems that use LLMs," you're looking at a different skill set. The biggest shift is thinking less about a single prompt and more about how an agent is orchestrated: what tools it has, how it plans, how it evaluates results, and how you test it.
A learning path that tends to work well is:
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Learn agent architecture before frameworks.
Understand concepts like:
- Planning vs. reacting
- Tool use (search, code execution, APIs)
- Memory (short-term vs. long-term)
- Reflection and self-critique
- Multi-step workflows
- Human-in-the-loop approval
These ideas transfer across frameworks.
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Study evaluation and harnesses.
A harness is essentially a repeatable environment for running an agent against many tasks and measuring how well it performs. Good harnesses let you:
- Replay the same tasks
- Compare different prompts or models
- Measure success rates
- Log intermediate reasoning, tool calls, and failures
- Regression-test changes
Without a harness, it's easy to improve one example while making overall performance worse.
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Build simple agents yourself.
Before using a large framework, create a small loop:
Goal
↓
Plan
↓
Call tool
↓
Observe result
↓
Decide next action
↓
Repeat until done
Building this once makes higher-level frameworks much easier to understand.
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Then learn a framework.
Some popular options include:
- LangChain for general agent orchestration
- LangGraph for stateful workflows
- OpenAI Agents SDK if you're working in the OpenAI ecosystem
- Microsoft AutoGen for multi-agent systems
Focus on understanding why they're designed the way they are rather than memorizing APIs.
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Learn evaluation alongside development.
Professionals spend a surprising amount of time on:
- Benchmark datasets
- Golden test cases
- Failure analysis
- Tool-call inspection
- Cost and latency measurement
- Safety testing
Great resources
If you prefer practical material:
- OpenAI Cookbook — examples of agents, tools, evaluations, and orchestration.
- LangGraph documentation — one of the clearest introductions to stateful agent workflows.
- DSPy documentation — teaches optimization and evaluation rather than prompt tweaking.
- OpenAI Evals — a good introduction to building evaluation harnesses.
- Inspect AI — an open-source framework focused on evaluating agents and model behavior.
A small project to cement the ideas
One of the best exercises is to build a "research agent" that:
- Takes a question.
- Searches the web.
- Chooses relevant sources.
- Summarizes findings.
- Verifies claims against the sources.
- Produces a final report.
- Records every intermediate step.
- Runs against a suite of 50 predefined research questions so you can compare versions.
By the end, you'll have implemented planning, tool use, state management, and—most importantly—a reusable evaluation harness.
If your goal is to work professionally with AI agents, I'd spend at least as much time learning evaluation and harnesses as prompt engineering. Strong agent developers typically rely on systematic testing and iteration rather than intuition about prompts alone.
