An AI agent is a system that pursues a goal by choosing actions in a loop: it decides what to do, calls a tool, reads the result, and decides again. That loop is what separates an agent from a single model call, and it is also why agents are harder to ship than they look in a demo. The core difficulty is compounding. A ten step trajectory where every step is usually right often produces a wrong final answer, because the chance of getting every step correct falls with each step added. Most of the craft is reducing that exposure: fewer steps, narrower choices, tools that are hard to misuse, and checkpoints where a mistake stops rather than propagates.
An AI agent is a system that pursues a goal by choosing actions in a loop: it decides what to do, calls a tool, reads the result, and decides again. That loop is what separates an agent from a single model call. It is also the reason agents are harder to ship than they look in a demo.
The core difficulty is compounding. A single model call that is usually right produces a usable result most of the time. A ten step trajectory where every step is usually right often produces a wrong final answer, because the chance of getting every step correct falls with each step added. Nothing about the model changed. The number of chances to go wrong did.
Most of the craft in agent development is reducing that exposure: fewer steps, narrower choices, tools that are hard to misuse, and checkpoints where a mistake stops rather than propagates.
Agent or workflow
The first decision is whether you need an agent at all. A workflow runs a fixed sequence with model calls embedded in it. An agent decides the sequence at runtime. Workflows are dramatically more reliable, cheaper to run, and easier to debug.
Workflow | Agent | |
|---|---|---|
Control flow | Fixed, written by you | Chosen at runtime by the model |
Reliability | Predictable, testable per step | Compounds across steps |
Cost and latency | Bounded | Variable, sometimes unbounded |
Debugging | Standard stack trace | Trajectory analysis |
Best for | Known process, variable content | Unknown process, variable goal |
If you can write down the steps, write down the steps. Reach for an agent when the sequence genuinely cannot be known in advance, which is rarer than current enthusiasm suggests. A large share of shipped "agents" are workflows with a model picking between two branches, and they are better products for it.
Shorten the loop
Every additional step is another opportunity to go wrong, so the highest leverage change is usually removing steps rather than improving the model.
Practical moves that work:
Give the agent one composite tool instead of five primitives it must sequence correctly. If the agent always calls search then fetch then parse, make that one tool.
Pre-compute anything that does not require judgment. Fetching the user's account state before the loop starts removes a step and a failure mode.
Cap the loop explicitly. A step limit, a wall-clock timeout and a spend limit should all exist before the first user touches it.
An agent that solves the task in three steps is not a less ambitious version of one that takes twelve. It is usually the better product.
Design tools the agent cannot misuse
Tool design is where most agent reliability is won or lost, and it looks much more like API design than like machine learning.
Name things for the decision, not the implementation. A tool called get_user_data invites wrong guesses about when to call it. look_up_customer_by_email does not.
Make the schema do the work. Enums instead of free text, required fields instead of optional ones, and types that make an invalid call impossible to express. Every constraint you encode is a class of error the model cannot make.
Return errors the model can act on. "Error 400" teaches nothing. "start_date must be before end_date; you sent start 2026-05-01 and end 2026-04-01" lets the next attempt succeed. Error messages are prompts, and they deserve the same care.
Make writes idempotent. Agents retry. If a retried call creates a second order, the retry logic becomes a business problem rather than an engineering detail.
Keep the action space small. More tools means more chances to pick the wrong one. Tools that are rarely correct to call should usually not exist.
Put a boundary before consequences
Reversibility should decide how much autonomy an action gets. Reading data, drafting content and running analysis are cheap to get wrong. Sending a message, moving money, deleting records and changing permissions are not.
A workable default: the agent may read freely, may write to a staging area freely, and must have a person confirm anything externally visible or irreversible. Autonomy on the consequential end gets earned with evidence from real usage, and it is much easier to widen a boundary later than to narrow one after an incident.
This mirrors the interface question covered in our piece on AI product design: the trust boundary is a design decision, and moving it inward after launch reads as a broken promise.
Make the trajectory visible
When a normal program fails you read a stack trace. When an agent fails you need the trajectory: the sequence of decisions, the tool calls, the arguments, the returned values, and the reasoning at each step.
Log all of it from the first prototype. Agent bugs are rarely a single broken function. They are a wrong turn at step four that looked locally reasonable, and you cannot diagnose that from the final output alone. Teams without trajectory logging end up guessing, and the guessing is expensive.
Two things worth alerting on: loops that hit their step cap, and tasks that finish suspiciously fast. Both usually indicate the agent gave up in a way the output does not reveal.
Evaluating agents
Agent evaluation has two layers, and both matter.
Outcome evaluation asks whether the final result was correct. It is what users care about and it is the number to optimise.
Trajectory evaluation asks whether the path was sensible: were the right tools chosen, were arguments well formed, was work repeated, did it recover from a failed call. A correct outcome reached by a chaotic path is fragile, and it will break as soon as the inputs shift slightly.
Build the evaluation set from real tasks, including ones that should fail. An agent that never declines is not careful, it is lucky, and the luck runs out. The scoping approach in our AI MVP development guide applies directly here: define what good looks like before building the loop.
Where agents work today
Honest assessment, based on the shape of the problem rather than on enthusiasm.
Agents tend to work when the environment is contained and errors are cheap: research and synthesis across documents, code changes inside a repository with tests to check them, data gathering and enrichment, triage and routing, and multi-step retrieval where each step is verifiable.
They tend to struggle when the environment is open ended and errors are expensive: long-running processes with no checkpoints, tasks requiring accurate real-world state the agent cannot observe, anything where a wrong action cannot be undone, and workflows where the correct answer depends on context the organisation never wrote down.
That last category is underrated. Many processes that look automatable are held together by knowledge that lives in people's heads. An agent cannot use context that was never recorded, and discovering that is often the real output of an agent project.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot responds. An agent acts. The distinguishing feature is the loop: an agent takes an action, observes the result, and decides what to do next, repeating until it reaches a goal or a limit. A chatbot that can call one function is closer to a workflow than an agent.
How many tools should an agent have?
Fewer than feels natural. Every tool widens the choice at every step, and wrong tool selection is a common failure. Start with the smallest set that can complete the task, and combine tools that are always used in sequence into one.
Do agents need a more capable model than other AI features?
Usually yes, because errors compound across steps, and a model that is adequate for a single call may not be adequate for twelve in a row. A cheaper approach is often to shorten the loop rather than upgrade the model, since fewer steps reduce the reliability requirement directly.
How do you stop an agent doing something harmful?
Constrain the action space rather than relying on instructions. If the agent has no tool that can delete records, no prompt can make it delete records. Combine that with confirmation before irreversible actions and hard limits on steps, time and spend.
Should an agent be able to write its own code and run it?
Only inside a sandbox with no access to production systems, credentials or the network unless specifically required. It is a genuinely useful capability for analysis and transformation tasks, and a serious liability without isolation.
Getting it right
Reliable agents come from narrowing the problem rather than expanding the model's freedom. Shorter loops, smaller action spaces, tools that resist misuse, a firm boundary before consequences, and enough logging to see what actually happened.
At Digcy we build AI products across software development, web app development and MVP development, including Quantillium.
If you are scoping an agent and want a second opinion on whether it should be an agent at all, get in touch.
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