To build a multi step AI agent, define a bounded goal, choose a planning approach that matches the task's branching complexity, wire tool calls with explicit input and output contracts, add a hard stopping condition before the first real run, and test against edge cases the happy path never exercises. The order matters: teams that skip straight to tool wiring end up bolting on scope and safety after something has already gone wrong.

This walkthrough builds one agent from a blank page: a research and summarization agent that takes a topic, searches for sources, extracts relevant facts, and produces a structured brief. The task is modest. The discipline required to build it correctly is not.

What Makes an Agent "Multi-Step" Rather Than a Single Prompt?

A single-step system takes one input, makes one model call, and returns one output. A multi-step agent decides, at runtime, what to do next based on what it just observed. It loops: plan, act, observe, revise, until a goal is met or a limit is hit.

That loop is the source of both the capability and the risk. A single prompt cannot search three sources, notice the first two disagree, and decide to pull a fourth. A multi-step agent can. But the same loop can spiral: calling the same tool repeatedly, misreading an error as success, or drifting from the original goal by step six of a run nobody is watching. The discipline below exists to keep the capability and remove the spiral.

Step 1: Define the Goal and Scope Before Writing Any Code

Every agent build should start with a one-paragraph goal statement precise enough that someone who never sees the code would know exactly what the agent is and is not supposed to do.

For the research agent, that statement is: given a topic and a target length, the agent finds three to five credible sources, extracts claims relevant to the topic, flags any claim it cannot verify against at least two sources, and returns a structured brief with citations. It does not publish, does not email anyone, and does not act on any source it cannot fetch successfully.

That last sentence is the scope boundary, and it does more work than the rest of the paragraph. Most agent failures in production trace back to scope that was implicit rather than written down. An agent given a general instruction like "research this and handle it" will eventually interpret "handle it" more broadly than intended, because nothing told it where the boundary was. Everything that follows in this walkthrough implements that paragraph. It does not substitute for it.

What Belongs in a Scope Statement?

A usable scope statement answers four questions:

  1. What input does the agent receive, and in what form.
  2. What output does it produce, and what does "done" look like.
  3. What actions are explicitly out of bounds, even if a tool exists to perform them.
  4. What happens when the agent cannot complete the goal: partial result, error, or escalate to a human.

If you cannot answer all four before opening an editor, the build is not ready. This is the step teams under time pressure skip, and its absence shows up as the most expensive kind of bug: the agent that did something technically successful and substantively wrong.

Step 2: Choose a Planning Approach That Matches the Task

Planning is how the agent decides the sequence of actions to take. The right approach depends on how much the plan needs to change in response to what the agent discovers mid-run.

Fixed sequence. The agent executes a predetermined series of steps: search, extract, verify, summarize. No step changes what future steps look like, only what data flows into them. This is right when the task shape does not vary, and it is the easiest to test and debug, since the control flow is not a variable.

ReAct-style reasoning and acting. The agent alternates between a reasoning step (what should I do next, given what I have observed) and an action step (call a tool), repeating until the goal condition is met. This suits tasks where the next action genuinely depends on the last observation, such as the research agent noticing two sources conflict and deciding to search for a third.

Plan-then-execute. The agent produces a full plan up front, then executes it, optionally revising if a step fails. This gives you a plan to inspect and approve before any tool runs, which matters when actions are costly or irreversible.

For the research agent, ReAct-style reasoning is the right fit: it needs to react to what it finds, not follow a fixed script. A fixed sequence would break the first time a search returned nothing useful and the agent needed a different query.

Why Does Overchoosing Complexity Cause Failures?

The most common early mistake is reaching for an open-ended planning loop when a fixed sequence would do the job with far less surface area for error. Every added degree of freedom is an added path needing testing and a stopping condition. Match the approach to the actual branching: a fixed sequence that works beats a reasoning loop that mostly works.

Step 3: Wire Tool Calls With Explicit Contracts

Each tool needs three things defined before the agent ever calls it: a name and description precise enough that the model reliably picks the right tool, a strict input schema, and a predictable output format, including what the output looks like on failure.

For the research agent, that means three tools: search(query) -> list of {title, url, snippet}, fetch_page(url) -> {text, status}, and extract_claims(text, topic) -> list of {claim, confidence}. Each returns a consistent shape whether it succeeds or fails, so the reasoning step always has something structured to work with rather than an unpredictable string to interpret.

The failure case is what practitioners most often leave loose. If fetch_page fails, does it return an empty string, throw, or a status field the agent can check. Pick one, document it, and tell the agent explicitly how to respond: on a fetch failure, try the next source rather than retry the same URL twice.

A Numbered Build Sequence for the Tool Layer

  1. List every tool the agent needs, named after what it does, not how it is implemented.
  2. Write the input schema for each tool first, so the calling contract is fixed and testable independent of the implementation.
  3. Define success and failure output as the same structure, so downstream reasoning does not need a separate code path for errors.
  4. Write a one-line description stating when to use each tool and when not to, since ambiguous descriptions are the most common cause of the wrong tool being called.
  5. Unit test each tool in isolation, outside the agent loop, before connecting it to the model.
  6. Only then wire the tools into the agent's tool list and run the first end-to-end test.

Testing tools in isolation catches bugs far harder to diagnose once buried inside a multi-step trace: a malformed schema, a silent exception, a tool returning nothing when it should return an empty list. Find those before the agent has to reason around them.

Step 4: Add a Stopping Condition Before the First Real Run

An agent without an explicit stopping condition runs until something external stops it: a timeout, a budget cap, or a human noticing. None of those are a plan. Write the stopping condition into the loop before the agent's first real task, not after the first runaway run.

A working stopping condition combines three checks:

  1. A maximum iteration count. A hard ceiling on how many plan-act-observe cycles the agent can run, set low enough during testing that a runaway loop fails fast and visibly. For the research agent, ten iterations is enough for a five-source brief; hitting the ceiling signals the plan or tools need work, not a number to raise reflexively.
  2. A goal-completion check. An explicit test, run after each step, for whether the Step 1 goal has actually been met, not just whether the agent claims it has. For the research agent: at least three sources with extracted claims, each checked against a second source.
  3. A no-progress check. A guard against the agent repeating the same action with the same input and getting the same result, the signature of a stuck loop, not a working one. If search runs with an identical query two iterations running, that is a stop condition, not a retry.

The iteration count alone is not sufficient: an agent can burn through ten iterations doing something subtly wrong the whole time. The goal-completion check is what tells you the loop stopped because it succeeded, not because it ran out of runway.

Step 5: Test Against Edge Cases Before Any Production Use

The happy path, where every search returns good results and every source loads cleanly, tells you almost nothing about whether the agent is safe to run unattended. Edge case testing is where a build earns or fails trust.

Run the agent against these five conditions, at minimum, before it touches anything production-facing:

  1. A topic with no good sources. Confirm the agent returns a partial result flagged as incomplete, rather than fabricating claims to fill the gap. This is the single most important test for any agent touching unverified external information.
  2. A tool that fails outright. Kill the search tool mid-run and confirm the stopping condition and error handling engage, rather than the agent looping indefinitely on a tool that will not respond.
  3. Conflicting sources. Feed it two sources that directly contradict each other and check whether the agent flags the conflict, per the Step 1 goal statement, instead of silently picking one.
  4. An ambiguous or overly broad topic. Confirm the agent narrows scope or asks for clarification rather than producing a shallow brief across too wide a surface.
  5. Adversarial or malformed input. Feed it a topic string containing instructions aimed at the agent itself, text that reads like a system prompt, and confirm it treats that as data to research, not as an instruction to follow.

Log every run, including the full sequence of tool calls and the model's stated reasoning at each step. When something goes wrong, the trace tells you whether the failure was in scope, planning, tool contract, or stopping condition.

Only after the agent handles all five conditions predictably is it a candidate for real workloads, and even then it should start on tasks where a wrong or incomplete output is recoverable, not on anything irreversible.

Key Takeaways

  • Write the goal and scope statement before any code. Most production agent failures trace back to scope that was implicit rather than defined.
  • Match the planning approach, fixed sequence, ReAct-style reasoning, or plan-then-execute, to how much the task actually branches, not to the most capable pattern available.
  • Give every tool a strict input schema and a predictable output shape for both success and failure, and unit test each tool in isolation before wiring it into the agent.
  • Combine a maximum iteration count, a goal-completion check, and a no-progress check. An iteration limit alone does not tell you the agent succeeded, only that it stopped.
  • Test against no-good-sources, tool failure, conflicting information, ambiguous scope, and adversarial input before any production use, and log full traces so failures are diagnosable by layer.

Practitioners who want this discipline formalized, including agent frameworks and orchestration, context and memory design, evaluation and observability, safety controls, deployment patterns, and cost optimization, assessed rather than self-declared, will find it covered in AICA's CAAP (Certified Agentic AI Professional) credential.