7 Common Mistakes When Building AI Agents for Business
Seven AI agent design checks covering scope, tool access, retry safety, evaluation, escalation, operating costs, and adversarial testing.
An agent needs a defined workflow, a limited set of tools, and a way to escalate uncertainty. These seven design checks address actions, retries, evaluation, operating costs, and adversarial testing.
1. Defining the Agent by What It Is, Not What It Does
Teams spend a lot of time choosing a framework and a model before they have written down what the agent will actually do. "We are building a sales AI agent" is not a definition. It is a category.
The useful definition looks like this: "The agent will receive an inbound lead from our CRM, retrieve the company's industry and size, score the lead against our ICP criteria, and either enroll it in a nurture sequence or assign it to a sales rep with a one-paragraph summary — without human review for leads scoring below 60."
Every action, every decision point, every handoff should be specified before a single tool is built. Teams that skip this end up building an agent that does a little of everything and not enough of anything.
2. Giving the Agent Too Many Tools
More tools do not make an agent more capable. Past a certain threshold, they make it less reliable. A language model reasoning over 30 tools on every step has more surface area for incorrect tool selection, hallucinated parameters, and unnecessary side effects.
Build the minimum tool set that covers the defined workflow. Add tools incrementally as specific gaps emerge. Remove tools that are never called in production. Treat the tool set as a product, not a catch-all.
3. Building Tools That Are Not Idempotent
A timed-out payment call may have succeeded even though the agent received no response. Retrying it can charge the customer twice. The same problem applies to messages and other external writes.
Every tool that writes to an external system must be safe to call more than once with the same inputs. This requires idempotency key support — either in your tool implementation or in the downstream API. It is not optional.
4. Measuring Inputs Instead of Outcomes
The number of deployed workflows does not show whether an agent is useful. Measure human handling time, correctly resolved cases, and cost per resolved interaction against a baseline.
Define your success metrics before the pilot starts. Run baseline measurements. If you do not have a baseline, you cannot know if the agent is improving anything.
5. Treating Escalation as a Failure Mode
When an agent escalates to a human, that is usually the right outcome for a situation the agent was not designed to handle. The mistake is designing escalation as a last resort — poorly surfaced, undocumented, without a handoff summary — rather than a first-class feature.
When escalation is an afterthought, the customer experience is bad: the customer has to repeat everything they told the agent, the human agent has no context, and nobody is tracking why escalation happened or how often.
Design escalation well and it becomes a quality signal. You see which situations the agent is not equipped for, and you can either improve the agent's capability for those cases or accept that some cases always warrant human judgment.
6. Not Accounting for Compute Costs at Scale
A reasoning-capable LLM call costs meaningfully more than a simple completion. An agent that averages 15 tool calls per workflow run costs meaningfully more per run than one that averages 5. Multiply that across thousands of daily runs and you have a line item that did not show up in your original build cost estimate.
Do the math before you scale. Estimate the average number of inference steps per workflow. Estimate the average number of workflow runs per day at your target volume. Multiply by the per-token cost at your expected context length. The number might be fine. Or it might tell you that your workflow needs optimization before you can run it profitably.
7. Skipping the Adversarial Test Phase
Before shipping an agent that interacts with users, someone needs to try to break it. Not in a theoretical way — in a hands-on way, spending a few hours trying:
- Asking it to do things outside its defined scope
- Providing false context to manipulate its actions
- Sending ambiguous requests that could map to multiple intents
- Pushing it to make commitments it is not authorized to make
- Injecting instructions through data the agent retrieves (prompt injection)
The goal is not to find every possible failure — it is to find the failure modes that are both likely and consequential. Fix those before launch. Document the known limits and handle them explicitly.
Review these checks before the pilot and again before expanding its traffic or permissions.