The regression you will not see coming
When you replace an LLM classification or decision step with Jev, the immediate numbers look good. Latency drops. Token costs fall. The pre-ship eval suite passes. What you do not have, in most setups, is any mechanism that applies the same outcome criteria to every production run before and after the swap and tells you whether the agent's actual behaviour changed.
That is a model change. It carries the same regression risk as swapping GPT-4o for Claude, or bumping a fine-tune from one checkpoint to the next. The fact that Jev is faster and cheaper than an LLM step does not make the downstream behaviour identical, and a fixed benchmark run against a curated dataset before deployment does not surface the difference. The gap between offline evaluation and what agents do in production is well documented, and it tends to widen exactly when you least expect it.
According to a Q1 2026 Forrester Agentic AI Wave survey, 22% of agent deployments report negative ROI at twelve months, with 41% of failures attributed to unclear success criteria and 26% to drift in evaluation coverage. Swapping a model component without continuous outcome measurement is how you contribute to both numbers.
Why pre-ship evals are not enough
A benchmark captures a snapshot. It tests the agent on the inputs your team thought to include, scored by criteria your team chose in advance, at one point in time. Production traffic is none of those things. It includes edge cases your eval set missed, user phrasing that shifts over weeks, and upstream data changes that your fixed test set cannot reflect.
When Notion's internal agents for ticket triage and issue resolution reached 10x throughput, from 3 to 30 issues resolved per day, that result came from a deployment that measured output against real work outcomes, not a pre-ship pass rate. The number is concrete because they measured something concrete in production. Most teams do not have that instrumentation in place before they make a component swap.
The problem compounds when the change is at a sub-step rather than the top-level model. A classification step that Jev now handles may feed a downstream reasoning step that is still LLM-driven. If Jev's output distribution differs even slightly from what the LLM produced, the downstream step can degrade without any single component appearing broken. Step-level accuracy failures cascade through multi-step agents in exactly this way, and a top-level success metric will not catch it until the degradation is large enough to show up in user-visible outcomes.
What a real before-and-after comparison needs
A comparison that can actually answer "did this get worse?" requires three things.
The same outcome criteria applied to both versions. Not accuracy on a held-out set. The criteria that define whether a production run succeeded: did the agent complete the task it was given, did it avoid the actions it should not have taken, did the output meet the quality threshold the downstream process requires. These criteria need to be defined before the swap, not reconstructed afterward.
Coverage of production traffic, not a sample. Every run on both versions should be scored. Sampling introduces selection bias and, more practically, makes it harder to detect regressions that affect only a subset of input types. Continuous evaluation in production, applied per-run rather than per-release, is the pattern that catches this class of problem.
A version-tagged trace for each run. Without version tagging, you cannot separate pre-swap runs from post-swap runs in your scoring data. This sounds obvious, but many teams retrofitting evaluation onto an existing agent find that their logging predates any concept of component versioning. Prefactor records a versioned span for each step in an agent's execution, which means you can query "show me all runs of the classification step on Jev v1 and score them against the same rubric as the LLM baseline."
The question the CTO has to answer
If you are signing off on this swap as a CTO or Head of AI, the technical question is actually a governance question: what evidence will you accept that the change did not degrade the agent, and who is accountable for producing that evidence?
Doctolib's engineering team, running Claude Code agents across 600 engineers for code review and feature shipping, found that adopting spec-driven workflows with explicit outcome criteria changed how they thought about model changes entirely. The question shifted from "does it feel right?" to "how many of the specifications does it complete, and at what rate?" That framing works for a Jev swap too. Define the spec, measure completion before and after, in production, on real tasks.
The same Forrester data that reports 22% negative ROI also notes that 88% of agent pilots never reach production, with 64% of the 12% that do convert citing evaluation and observability as the largest blocker. The teams that get through that barrier are the ones who treat evaluation as infrastructure, not as a pre-release gate.
Detecting quality decay before it reaches a threshold that affects users requires ongoing measurement, not a one-time check. An activity schema that defines what the agent should and should not do gives you a persistent reference point. Validating agent behaviour against that schema on every run is what separates a deployment you can defend from one you are guessing about.
The silent regression problem
The worst outcome of an unmonitored Jev swap is not an obvious failure. It is a quiet shift in the classification or decision output that degrades a downstream metric over weeks, by an amount small enough that it blends into normal variance. By the time you notice, you have weeks of production data to unpick and no clean baseline to compare against.
Silent quality decay in production agent deployments follows exactly this pattern. The agent still runs. It still returns outputs. Nothing throws an error. But the outcome quality has moved, and you find out because a business metric changed, not because your evaluation system caught it.
Prefactor scores each production run against your defined quality and risk criteria and attaches that score to the versioned span, so the before-and-after comparison is available immediately after the swap goes live rather than reconstructed from incomplete logs weeks later. That is not a guarantee the agent did not regress. It is a guarantee you will know if it did, and how much.
Where to start
Define your outcome criteria for the classification or decision step before you swap it. Instrument the agent so that every production run generates a versioned trace with step-level scores against those criteria. Then make the swap, and compare.
Start evaluating your agents or read the docs for guidance on setting up versioned spans and production scoring.