What Are AI Evals and Why Do Agentic Workflows Need Them?
An AI evaluation, or eval, is a repeatable test of a defined system behaviour. It gives the system a task, records what happens and applies explicit criteria to the available evidence. For an agentic workflow, the evaluation must establish more than the quality of a response. It must show whether the agent used information, tools and authority correctly and whether the required business outcome occurred.
A convincing demonstration is not evidence of reliability
An AI agent can perform an impressive task during a demonstration. However, one successful attempt does not show how the agent behaves when records are incomplete, a tool fails, a user changes the request or an action requires approval. Management cannot infer dependable performance from a selected example.
Informal testing creates the same problem at a larger scale. Different reviewers choose different prompts and judge results by different standards. When a model, prompt or tool changes, the team cannot tell whether the new system improved, regressed or merely encountered easier cases.
Evals replace this impression with comparable evidence. They define the task, conditions, expected behaviour, grading rule and system version before the result is known.
An eval contains a task, trial, trace, outcome and grader
The task states what the system must do and the conditions under which it must do it. A trial is one attempt at that task. Because agent behaviour can vary, an evaluation may run several trials of the same task.
The trace records the interaction, including messages, tool calls, intermediate results and handovers. The outcome records the final state, such as whether a refund exists in the ledger or an approval request reached the right employee. A grader applies defined criteria to the trace, outcome or both.
These distinctions matter when a result fails. The task may be ambiguous, the environment may be broken, the agent may choose the wrong action or the grader may reject a valid solution. A useful evaluation preserves enough evidence to locate the cause.
Agentic workflows create linked failure paths
A single model response can be assessed for qualities such as correctness, relevance or harmful content. An agentic workflow adds retrieval, memory, planning, tools, permissions, state changes and handovers. An early error can influence every later action.
For example, a service agent may correctly identify that a customer qualifies for a refund but select the wrong order. It may call the correct refund tool with an excessive amount. It may complete the refund and then repeat it after a timeout. It may also make no system change while telling the customer that the refund succeeded.
Response quality cannot distinguish these failures. The eval must inspect the evidence used, the tools and arguments selected, the authority applied and the final business state.
Different questions require different evals
Capability evals ask whether the agent can complete the intended task. They reveal the boundary of what the system can do under defined conditions.
Control evals ask whether the agent respects policies, permissions and escalation rules while completing the task. Reliability evals repeat cases and change relevant conditions to estimate how consistently the behaviour holds. Security evals introduce hostile instructions, manipulated records or attempts to obtain unauthorised action.
Business evals connect system behaviour to operating consequences. They may measure completed cases, human correction, delay, cost or customer impact. These measures should not replace technical evidence. They show whether technically acceptable behaviour accompanies the intended operating result. They do not establish that the agent caused the result unless the organisation uses a comparison or experiment capable of supporting that conclusion.
The evaluation team combines these tests according to the proposed use. A low-risk drafting assistant does not require the same controls as an agent that can update payments or customer records.
Every eval should support a decision
An eval becomes useful when management states what the result will change. A capability result may determine whether development should continue. A control failure may reduce the agent's permissions. A reliability result may require human approval for specified cases. A regression failure may block a release.
The acceptance rule should exist before the test runs. Otherwise, the team can reinterpret an inconvenient result after seeing it. Critical events should remain separate gates rather than disappear inside an average score.
The decision also determines the evidence required. A development comparison may tolerate a small visible test set. A release decision needs representative and held-out cases, validated graders and reproducible environments. Production monitoring needs current traces and incident feedback because operating conditions change after deployment.
Evals improve the system throughout its lifecycle
Before development, eval cases clarify what successful behaviour means. Two engineers may interpret the same requirement differently; a solvable case with observable criteria exposes the disagreement before it enters the design.
During development, evals compare prompts, models, tools and workflow changes against the same cases. After release, reviewed incidents and near misses become regression tests. The team then preserves those cases in the regression suite so that a later change cannot silently reintroduce a known failure.
Evals do not replace production monitoring, user research or controlled business experiments. Automated tests find known forms of failure quickly. Monitoring reveals changes and unanticipated incidents. Human review calibrates subjective judgements. Business experiments establish whether the deployed workflow improves the intended outcome.
Example: an invoice-exception agent
Consider an agent that prepares supplier invoice exceptions for a finance reviewer. One case contains a purchase order, receipt, invoice, supplier record and approval limit. The payment tool acknowledges a request but withholds the final ledger state after a timeout.
The agent should recognise that payment remains unverified. It should avoid a duplicate instruction, preserve the case and refer it to the authorised finance owner. The trace shows the evidence and tool calls. The final database state shows whether a duplicate payment instruction exists. A grader checks whether the explanation matches that state.
Repeated trials show whether the agent applies the control consistently. Boundary cases change the approval amount, supplier status or timing of the ledger update. The evaluation can then establish both useful completion and justified restraint rather than reward an agent that always acts or always refuses.
What Marketways delivers
Marketways identifies the decisions that the agent must support, the failures that could change their outcome and the evidence required to detect those failures. We design tasks, controlled environments, graders, repeated trials and release gates around the actual workflow.
The resulting evaluation protocol states what was tested, what the evidence establishes, which conditions remain outside scope and which result should approve, restrict or stop deployment. This turns evaluation from a demonstration score into a management control.