Ask an agent to reconcile a month of vendor invoices and it will sometimes do something genuinely clever: notice that two entries share a tax ID, infer they’re the same supplier under a trading name, and merge them. Ask it the same task two weeks later and it will merge the wrong pair, then explain its choice in fluent, confident prose.
Nothing about the model changed. What changed was the reasoning happening between the instruction and the button press. That layer is where AI agent reasoning lives, and it’s the part most teams underestimate when they move from a demo to something running unattended.
Reasoning isn’t the same as thinking out loud
A language model will happily narrate a tidy step-by-step rationale for whatever it just did. That narration is not proof the reasoning happened. Ask an agent why it skipped a verification call and it may tell you the endpoint was unavailable. Check the trace: the call was never attempted.
This matters because a lot of what gets sold as reasoning is really just longer output. The useful distinction is between text that sounds like deliberation and a process that actually reshapes the next action. If you want a sharper line between ordinary software and something that plans, this explanation of what “agentic” adds to the word AI is a reasonable place to start.
Three jobs reasoning actually does in an agent
Turning a vague goal into a bounded plan
“Tidy up the CRM” is not a task. Reasoning is what converts it into something executable: find duplicate contacts, merge where confidence is high, flag the remainder for a human. Weak reasoning produces plans so loose that the executor has nothing concrete to do with them.
Picking a tool and filling in its arguments
Choosing the right function is only half the job. The arguments matter just as much, and a plausible-looking but wrong parameter (yesterday’s date, the wrong account ID, a truncated identifier) executes cleanly and writes garbage into production. Function-calling benchmarks rarely catch this because the call itself succeeds.
Knowing when to stop
Stopping is a reasoning act, not a triviality. Agents that loop, re-check the same record four times, or declare victory at sixty percent completion are usually missing an explicit finish condition rather than a capability.
The maths of a long task are brutal
Suppose your agent chooses the right next step 95% of the time. That feels strong. Run it for 20 steps and 0.95 to the power of 20 lands you at roughly 36%. Nearly two thirds of runs will contain at least one wrong turn.
Most of those wrong turns are recoverable, if the agent notices. Few notice reliably. A bad intermediate result gets carried forward as established fact, and the remaining plan is built on top of it. We dug into that pattern in where an AI agent workflow quietly breaks, and it explains most cases of a workflow that demoed beautifully and then fell over in production.
More reasoning is not automatically better
There’s an instinct to give the agent more thinking time whenever it misbehaves. Extra test-time compute does help on genuinely hard, single-shot problems. On long agent tasks it often makes things worse: agents drift, re-litigate settled decisions, and invent new sub-goals to justify the extra tokens they’ve been given.
Two arrangements tend to hold up. Plan once, then execute with checkpoints. Or keep steps short and re-plan only when something fails. Where the planner sits relative to the executor, and where state lives between them, accounts for a surprising share of the outcome, which is the argument running through this piece on the architecture decisions that actually decide whether an agent works.
Where reasoning tends to break
- Fuzzy tool descriptions. Two functions with overlapping purposes will be confused roughly in proportion to how similar their descriptions are. Rewriting them is often worth more than swapping models.
- Long transcripts. By step 30, the original instruction is a distant memory and the agent is optimising against its own recent summary of the goal.
- Confidently wrong facts. A hallucinated order number looks identical to a real one in the context window. Nothing downstream flags it.
- Ambiguous success criteria. If “done” isn’t testable, the agent will decide for itself, usually generously.
- Room to improvise. Broad permissions invite creative workarounds that no one specified and no one tested.
Several of these read like reasoning failures but are really specification and permission problems wearing a costume. There’s a useful catalogue of the realistic failure modes in what actually goes wrong when software starts acting, and most of them are cheaper to fix than they look.
How to test reasoning you can’t see
You can’t unit-test a thought process, but you can test its consequences. A few techniques earn their keep:
- Golden task sets. Twenty to fifty real tasks with known-good outcomes, run on every prompt or model change. Watch for regressions in the arguments, not just the final answer.
- Injected failure. Make a tool return a 500 mid-run and see whether the agent notices, retries sensibly, or plows ahead with an empty result.
- Order shuffling. Feed the same data in a different sequence. If the outcome flips, the agent is reasoning from position rather than content.
- Trace review. Read the tool calls, not the narration. The narration is generated after the decision, so it will always sound reasonable.
Honest expectations help here too. A clear-eyed look at what AI agents can actually do now is a useful calibration before you build a test suite around capabilities the model doesn’t have.
Design choices that give reasoning a fighting chance
Narrow the toolset. An agent with six well-differentiated functions outperforms one with thirty overlapping ones, consistently. Write descriptions that state when not to use a tool, not just what it does.
Ask for structured intermediate output: a plan as a list of steps with a named tool and expected result for each. Structure forces specificity in a way that free-form reasoning doesn’t, and it gives you something to log and compare across runs.
Store facts, not narrative. Memory that holds “invoice 4471 verified, amount 2,340 GBP” is reusable. Memory that holds three paragraphs of the agent’s musings mostly adds noise to the next prompt.
Put a checkpoint before anything irreversible. Sending, deleting, paying, and publishing all deserve a confirmation step, either a human gate or a deterministic rule that re-checks the preconditions independently of the agent’s claim that it already did.
The tell of a well-built agent is boring in a good way: short traces, few tool calls per step, and failures that surface at the step where they happened rather than six steps later. When the trace shows an agent re-reading the same record three times, or calling a search tool with a query it already ran, that’s the reasoning losing its grip. Catching it there is far cheaper than catching it in the output.