Key skills needed to develop advanced AI agents

Most posts on this topic name frameworks. LangChain, LangGraph, CrewAI, whatever shipped this month. Frameworks change every quarter; the underlying skills do not.

Here are the seven that pay off across model generations, in roughly the order they start to matter. Half of them are not AI skills at all.

1. Reading LLM behavior, not chasing model releases

This is deeper than prompt engineering. It is understanding why a model hallucinates on long context, why structured JSON output breaks past a certain length, what temperature actually changes and what it does not.

Beginners upgrade to the newest model and hope it fixes their agent. Builders understand the failure modes of the model they already have, which is usually enough to fix the agent without changing anything else.

If you cannot name three specific ways your current model tends to fail on your task, you do not yet know it well enough to build on top of it.

2. Tool design as API design

The tool schema is the interface between the model's language and the rest of your system. It is not a detail — it is where most agents break.

Vague names (process_data), overlapping tools (search and find both do lookup), no useful error strings coming back — every one of these makes the agent wander or repeat itself. Good tool design looks a lot like good SDK design: clear verbs, one job per tool, error messages a stranger could act on.

If you find yourself adding tools to fix bad behavior, look at the tools you already have first. Nine times in ten the fix is a better description on an existing one.

3. Evals — or you are guessing

Most builders skip this and rely on vibes. A change that "feels better" in one demo silently breaks three other cases you never noticed.

The skill is not sophisticated eval frameworks. It is discipline: write down twenty realistic inputs, mark the expected behavior, run them on every prompt change, and refuse to ship when the score drops.

Nothing separates hobby projects from production agents more than this. When your model provider bumps you to a new version tomorrow, evals are the only reason you will notice something regressed before your users do.

4. Memory and context management

What to keep in the prompt, what to summarize, what to throw away. A vector database is one tool for this, not a default answer.

The skill is matching the memory shape to the task. For an agent running a linear task with twenty steps, a plain append-only log in the prompt is usually better than an embedding search — the last five steps are always the most relevant, and there is no ambiguity about what "relevant" means. Reach for the vector store when the retrieval question is genuinely a similarity question, not a recency one.

5. Boring software engineering

Agents are software. The skills you need are the skills you already needed:

• Structured logging on every step, so you can actually debug a failure.
• Retries that are idempotent, so you can safely re-run a partial trajectory.
• Prompts checked into version control alongside the code that calls them.
• Tests on the deterministic parts (tool wrappers, parsers, guards), so the LLM is the only thing you have to check by hand.

This is the skill most beginners assume they can skip because "it is just a script." Six months later the script is a critical piece of infrastructure with no logs and no way to reproduce yesterday's bug.

6. Cost and latency as design constraints

A loop that runs two hundred times costs two hundred times. Long context is quadratic in most billing.

The skill is designing an architecture where cost is bounded up front, not optimized after the bill lands. Caching what does not change between steps. Summarizing older turns instead of dragging them along. Building early-exit conditions into the loop so a done agent stops instead of thinking about being done for five more turns.

An agent that costs two cents per run in your demo will cost two thousand dollars the day it runs against real traffic. Design for that number.

7. Security thinking, and prompt injection especially

Any text that reaches the model is untrusted input, even if it came from your own tool. A web page returned by a search tool can contain instructions that hijack your agent. A row from your own database can too, if a user put it there.

The skill is a boundary mindset. Tool outputs are data, not instructions. Structured outputs beat free-form text at every boundary you can control. The answer to "what if the model does something bad" is usually a permission check in the tool, not more words in the system prompt.

Anyone shipping an agent that touches money, email or shared systems needs to be able to reason about this without prompting.

The one that is not on the list, but should be

The judgment to not build an agent at all.

Many problems do not need one. A script, a cron job, a form, or a single well-prompted LLM call is often cheaper, more reliable, and dramatically easier to fix. Every skill above helps you build a good agent; this one helps you skip the ones you should not build.

If the loop can be a straight line, make it a straight line. Start with the smallest thing that solves the task and only add a loop when the task actually branches. See Building your first AI agent from scratch for how small that can be.

Closing

None of the seven skills are secret. What is uncommon is the willingness to spend real time on the boring half — evals, logs, cost, security — before the agent looks impressive.

If you build one that works, list it on AgentBoard. If it stops working, the board will show that too.