AI agent evaluation in Austin.

The technical reality of evaluating multi-tenant AI in Austin.

Questions from Austin buyers.

Our AI feature works great for some customers and badly for others. How does evaluation catch that?

By slicing every metric per tenant segment, because multi-tenant averages are where per-customer failures hide. The mechanics: eval sets get built with tenant-representative coverage (data shapes, industries, usage patterns sampled across your base, not just your three favorite design partners), and production sampling scores outputs tagged by tenant cohort. The reporting then shows quality distribution, not just central tendency: median tenant experience alongside the bottom decile, flagged cohorts where scores diverge (the customer whose documents are scanned faxes, the one whose product taxonomy is in Spanish, the one whose tickets are twice the median length). That bottom-decile view is the churn-prevention instrument: accounts experiencing degraded AI quality rarely complain first; they quietly stop using the feature, and the usage drop shows up in renewal risk two quarters later. We typically find two or three tenant cohorts whose experience differs materially from the average, each fixable once visible.

Our support-deflection bot reports 60 percent containment. Is that number real?

Possibly, but containment numbers are the most gamed metric in the category, and the audit checks three failure modes. Abandonment counted as success: a user who gave up and left did not get deflected; they got lost, and honest containment separates resolved (the user's issue demonstrably addressed) from exited (gone, outcome unknown) using follow-up signals: did they reopen, email, or call within N days. Misrouted resolution: the bot marked it answered, the user immediately contacted support anyway; cross-channel joins catch this, and the leakage is often a third of claimed containment. And selection effects: if the bot only fields easy intents while hard ones route straight to humans, 60 percent containment of the easy half is a different business case than the dashboard implies. The deliverable is a containment number with provenance: resolved-without-recontact rate by intent class, measured against cross-channel data, which is the number a CFO can multiply by ticket cost without embarrassment.

What does guardrail testing actually involve for a customer-facing AI feature?

Adversarial suites run like security testing, with the same assumption: someone will try it. The categories we build for Austin SaaS features: prompt injection (instructions embedded in user content or uploaded documents attempting to override system behavior; the suite includes the current public injection corpora plus payloads crafted against your specific tool and data access), data-boundary probes (attempts to extract other tenants' data, system prompts, or credentials through the feature), policy compliance under pressure (refusals that hold across rephrasing, roleplay framing, and multi-turn erosion), and brand-safety behavior (tone and claims under hostile or absurd inputs). Each category gets pass-rate tracking over time, because guardrails regress silently when prompts change. The honest framing: no suite proves safety exhaustively; what it proves is that the known attack classes fail and that regressions in that property get caught before customers find them, which is what your enterprise buyers' security teams are actually asking about.

How should evaluation gate releases without slowing our ship cadence?

By matching gate weight to blast radius, the same way you already treat database migrations differently from copy changes. The policy we install: prompt tweaks and few-shot edits run the smoke tier (high-signal subset, minutes, auto-pass on no-regression); model swaps, retrieval changes, and new tools run the full eval plus guardrail suites (hours, human review of per-slice diffs); and new capabilities or new data access run the full battery plus a canary rollout with production scoring before general availability. Two design choices keep velocity intact: gates report per-slice diffs rather than binary pass-fail (so a known, accepted trade-off does not block a ship; it gets documented and waved through), and the override path is explicit with named ownership, because a gate that cannot be consciously overridden becomes a gate that gets quietly disabled. Teams typically find the smoke tier catches the embarrassing regressions and the full tier catches the expensive ones, while ship cadence stays exactly where it was.