A working AI agent takes about forty lines of code. A reliable one takes a quarter, an evaluation suite, and someone on call. That gap is what AI agent development is really about, and it is where most teams get caught out.
The demos look great. The model reads a support ticket, calls a search tool, drafts a reply, hits send. Then production arrives. A malformed JSON argument. A tool that times out at exactly the wrong moment. A customer who asks for something just outside the schema. The agent loops, burns fourteen dollars of tokens, and quietly files a ticket of its own.
What AI agent development actually covers
Strip away the marketing and an agent is a language model with a goal, a set of tools, and permission to act in a loop until the goal is met. If you want the mechanical version, how software learned to get things done on its own lays out the core idea. What matters for development is the difference between that and a workflow.
A workflow is a flowchart the model happens to sit inside. You know the paths in advance. An agent decides its own path, which is exactly why it handles messy inputs and exactly why it can surprise you. Most production systems land somewhere in between: a fixed skeleton of steps with one or two decision points where the model gets latitude. That hybrid is usually the right call. Full autonomy sounds better in a pitch deck than it behaves in a billing system.
The four moving parts, and where the work hides
Every agent you build has the same anatomy: a model, a tool layer, some form of memory, and an orchestration loop that decides what happens next. The model is the easy part now. It’s the other three that eat the schedule, and the loop that turns models into doers is worth reading before you start drawing diagrams.
Tool design is where most projects stall
A tool is an API call with a description. That description is a prompt, whether you treat it that way or not. If your search_orders tool says it searches orders, the model will pass a customer name and hope. If it says it takes an order ID in the format ORD-###### and returns the last ninety days, failure rates drop immediately.
Practical rules that save weeks:
- Return errors the model can read and act on. “Invalid date, expected YYYY-MM-DD” beats a 500 every time.
- Cap list responses. Returning 4,000 rows wastes context and pushes the interesting detail out of the window.
- Give each tool one job. A tool that “manages the account” will be used for the wrong half of that job.
- Log every call with inputs and outputs. When something breaks at 2am, that log is the only thing standing between you and guesswork.
Memory should stay boring
There is a strong temptation to build a beautiful memory system. Resist it. For most business agents, the useful memory is the conversation transcript plus a small key-value store of confirmed facts, like a customer ID or a preferred format. Vector databases earn their place when you’re searching a large document corpus, not when you’re remembering someone’s timezone.
Write the loop before you reach for a framework
Frameworks promise orchestration, state, and retries. They also hide the failure modes you most need to understand. A better order of operations: build the dumbest possible loop yourself first, watch it break, then adopt a framework once you know which problem you’re paying it to solve. Starting with a short Python loop and hardening it is a far faster path to production than wiring four libraries together on day one.
That first loop should have a hard iteration cap, a token budget, and a timeout. Not because you expect to need them, but because the first runaway agent teaches an expensive lesson. Pick a number. Twenty steps is generous for most tasks.
Evaluation is the part nobody budgets for
You cannot ship an agent you can’t measure, and “it looked good in testing” is not a measurement. The minimum viable setup is a golden set of real inputs, somewhere between 100 and 300, with expected outcomes. Half of them should be annoying: sparse data, ambiguous phrasing, requests that should be refused.
Then decide how you score. Exact match works for structured output. For free text, a second model acting as a grader gets you surprisingly far, especially if you spot-check 20% of its verdicts by hand. Run the whole suite on every prompt change. Yes, every time. Prompt edits feel cosmetic and behave like code changes.
Guardrails and the blast radius problem
The question that should drive your architecture is simple: what’s the worst thing this agent can do if it has a bad day? An agent that drafts emails can embarrass you. An agent with production database write access can delete a table.
Scope credentials down to the specific actions the agent needs. Put a human approval step in front of anything irreversible, anything that moves money, and anything that contacts a customer for the first time. Make write operations idempotent so a retry doesn’t double-charge someone. These are unglamorous engineering habits, and they are the entire difference between an agent you leave running and one you babysit.
Build, buy, or glue
Plenty of teams build an agent that already exists as a product. Before you write anything, check what’s on the market, because the honest answer for a lot of internal use cases is to buy. Sorting the useful business agents from the expensive noise is a decent starting filter, and smaller teams should look at which agents are worth paying for at startup scale before committing headcount.
Build when the agent touches your proprietary workflow, your data, or your customers in a way no vendor can replicate. Glue when a vendor covers 80% and you need a thin layer on top. Buying the 80% and building the rest is usually cheaper than either extreme.
The running costs nobody mentions
Agents are chat models with a spending habit. A single task might involve six model calls, two tool lookups, and a retry, which turns a one-cent interaction into fifteen. Latency compounds the same way. Users tolerate a two-second answer and start clicking around at eight.
Three levers do most of the work. Route easy steps to a smaller, cheaper model and reserve the big one for genuinely hard reasoning. Cache anything recomputable. Stream output so people see progress instead of a spinner. Add per-task token ceilings, and alert when an agent’s average cost per task drifts upward, because that is usually the first symptom of a prompt change gone wrong.
Agents are moving into unglamorous places
The interesting work is no longer all in customer support. It’s in procurement, logistics, lab notebooks, and supply chains, where the data is messy and the payoff is measured in weeks saved rather than retweets. Scimplify’s $25M raise to rethink critical materials sourcing with AI is a good example of the pattern: narrow domain, deep tooling, unglamorous inputs, real money on the line.
That’s the shape most AI agent development will take over the next few years. Not general assistants that do everything adequately, but small, well-instrumented agents with tight permissions, a clear job, and a log file you can read when things go sideways. Pick one narrow task. Build the loop by hand. Measure it against a hundred real cases. Then, and only then, let it run unsupervised.