Evals That Keep Your GTM Agents Honest
An agent that emails your prospects, posts as your brand or updates your CRM is production software with your reputation attached, and production software gets tested. We build evaluation systems for GTM agents: accuracy, tone, safety and outcome benchmarks that run before every change and continuously in production.
Key Facts
- Focus
- ai agent evaluation
- Category
- Agent Ops & Infra
- Defined outputs
- 6 deliverables
- Regions served
- India · United States · United Kingdom · UAE · Singapore
- Last reviewed
- 2026-09-10
"The Demo Looked Good" Is Not a Quality Bar
Most teams deploy GTM agents the way nobody would deploy code: watch a few outputs, feel good, ship it, find out about failures from prospects. Then a model update quietly changes behavior, a prompt tweak breaks tone, and the first sign of trouble is a screenshot of your agent's email going viral for the wrong reason. Agents don't fail loudly at deploy time. They drift quietly in production.
Vibes-based QA: quality assessed by skimming a handful of outputs, a sample that catches almost nothing about tail behavior, where reputational damage lives.
Silent regression: model updates and prompt changes shift behavior with no changelog; without regression tests, yesterday's safe agent is today's liability.
Unanswerable audits: when security, legal or an enterprise customer asks 'how do you know the agent behaves?', 'we watch it sometimes' ends the deal.
CI for Agents: Test, Gate, Monitor
Behavior specification
We turn 'be on-brand and accurate' into testable assertions: factual claims the agent may and may not make, tone boundaries, escalation triggers, compliance rules per market. If it matters, it's written down and checkable.
Eval suite construction
Golden datasets of real scenarios, including the adversarial and edge cases that break agents, scored by graded rubrics: automated checks where possible, LLM-judged with human-calibrated rubrics where judgment is needed, humans on the cases that matter most.
Gated deployment
No prompt change, model swap or workflow edit reaches production without passing the suite, exactly like CI for code. Scores are tracked per version, so you can see quality trend across every change and roll back with evidence.
Production monitoring and drift detection
Sampled live outputs are continuously scored against the same rubrics, with alerting when quality drifts, which catches the quiet regressions that scheduled testing misses, before prospects catch them for you.
What This Changes for Your Business
Autonomy you can widen safely
Teams under-use agents because they can't trust them. Eval scores are how trust gets earned measurably, so you can widen automation on evidence instead of anxiety.
Enterprise-ready answers
When procurement, security or legal asks how agent quality is assured, you hand them eval methodology and score history, which is often the difference between passing review and losing the deal.
Faster iteration, not slower
Counterintuitively, gates speed you up: with a regression suite guarding behavior, your team can change prompts and models aggressively instead of fearing every edit.
Deliverables
- Written behavior specification per agent
- Golden-dataset eval suites with adversarial and edge cases
- Human-calibrated scoring rubrics and automated graders
- Deployment gating integrated into your agent change workflow
- Production sampling, drift detection and alerting
- Versioned score history and audit documentation
Is This You?
Strong fit
- Teams running agents that touch customers (outreach, social, support, CRM writes), whether we built the agents or someone else did
- Companies selling into enterprises where automation must survive security and procurement review
- Anyone burned once by silent agent drift and determined not to repeat it
Not a fit yet
- Purely internal, low-stakes automations, where a failed draft nobody sends doesn't warrant this rigor
- Teams wanting a rubber stamp on agents they refuse to change, because evals exist to drive fixes
- Prototype-stage agents still changing daily by design. Spec first, then eval
Find Out What Your Agents Actually Do at the Edges
Bring one production agent to a strategy call. We'll design a starter eval for it live, and show you the categories of behavior you're currently not seeing.
Book a 30-Min Strategy CallSend a Request
We'll be in touch!
Expect a call within 1 business day.
Common Questions
What is Agent Evaluation?
Agent Evaluation is structured testing for GTM agents against accuracy, tone, safety and outcome benchmarks, run before every change and continuously in production, so automation earns trust before it touches customers.
We just review outputs manually. Why isn't that enough?
Manual review samples a sliver of behavior at one point in time. It misses tail cases, the weird inputs where agents embarrass you, and it can't catch drift after model updates. Structured evals cover the space systematically and keep covering it every time anything changes.
Do you evaluate agents you didn't build?
Yes. The practice is deliberately independent of who built the agent. We evaluate Kirality-built systems, in-house agents and third-party tools alike; if anything, independent evaluation of vendor agents is where clients find the most surprises.
What does 'LLM-as-judge' mean and can it be trusted?
It means a model scores outputs against a rubric, which works only when the rubric is calibrated against human judgments, which is exactly what we do: humans grade a calibration set, the judge is tuned to agree with them, and agreement is re-checked over time. Uncalibrated LLM judging is how teams fool themselves; calibration is the discipline.
How often do evals need to run?
On every change (gating), and continuously in production (drift sampling). The expensive part, building specs, datasets and rubrics, is one-time with light maintenance; running them is cheap and automatic. That asymmetry is why this pays for itself.
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