Evaluate complete behavior
Converra scores full multi-turn conversations across task completion, accuracy, tone, safety, and custom business metrics — not a single generated answer.
LLM agent evaluation
Evaluate agents in the same shape they fail
Converra evaluates LLM agents across full multi-turn conversations on the metrics that matter, connects each score to a root cause, tests fixes head-to-head against the baseline, and verifies production impact.
Production proof
Salespeak orchestrator agent
100%
Hallucinations eliminated
The orchestrator stopped fabricating pricing, VAT rules, and infrastructure details — issues users were relying on as fact. Zero occurrences verified across production traffic since Apr 23 deploy.
74%
Fewer routing failures
Mis-routed queries — users landing with the wrong specialist — dropped 74% across production traffic after the Apr 25 deploy. Verified.
0
Engineering hours
Converra generated and tested the fixes; Salespeak's CTO reviewed and applied the winning changes.
A score is not an improvement plan
Agent teams need evaluation, but a low score only starts the work. The useful output is an evidence-backed path from failed behavior to tested change to verified production lift — a framework, with the right metrics, that ends in a fix.
Metrics that matter, not vanity scores
Default quality dimensions — goal achievement, sentiment, clarity, relevancy — plus use-case metrics like routing accuracy, lead qualification, escalation correctness, and policy adherence.
Tie scores to root causes
The report does not stop at a low score. It identifies the step, turn, failure mode, and change direction behind every meaningful failure.
Head-to-head, not absolute
Candidate fixes are scored against the baseline on the same personas and scenarios — never on baseline-only data — so a higher number reflects real lift, not an easier draw.
Agent evaluation, not just LLM evaluation
Multi-agent systems are evaluated at the system level: handoffs, routing, and contribution per node. The orchestrator is usually the weakest link, and single-agent scoring misses it.
Keep evaluation connected to production
After deployment, Converra checks whether the measured improvement survived contact with real users — the verdict no eval dataset can give you.
The evaluation workflow
Converra is built for teams that already have agents in production and need a repeatable way to improve them without handing every failure back to engineering.
- 1Import or collect representative production conversations.
- 2Score the agent across quality, safety, and business-specific metrics.
- 3Classify the root cause behind each meaningful failure (step, turn, failure mode).
- 4Generate and evaluate candidate fixes head-to-head against the baseline.
- 5Run a free, no-login audit at /eval to see the evaluation on your live agent in minutes.
- 6Use production verification to confirm the fix changed real behavior.
Evaluation as an input to improvement
Use Converra when you need evaluation to drive action: which behavior failed, what should change, whether the change is safer, and whether it worked after shipping. Start with the free audit at /eval, then close the loop.
Related reading
Free agent eval
Audit any production AI agent in ~10 minutes — adversarial probes, transcripts, and prompt-level fixes, no integration required.
Agent failure modes
The AFM taxonomy (AFM-01–AFM-16) that turns a low evaluation score into a named, fixable failure mode.
Simulation testing
How Converra tests candidate fixes through multi-turn simulated conversations before deployment.
Production verification
How Converra proves a deployed fix worked with before/after production evidence.
Salespeak case study
Evaluation-to-verified-fix in production: hallucinations eliminated, routing failures down 74%, zero engineering hours.
AI agent glossary
Canonical definitions for evaluation, lift, evidence level, head-to-head comparison, and related terms.
FAQ
What is LLM agent evaluation?
LLM agent evaluation measures whether an agent completes the right task safely and accurately across realistic, multi-turn conversations. It is broader than judging one generated answer: it covers task success, tone, safety, and business outcomes over a whole conversation, and — for multi-agent systems — the handoffs between agents.
What is an LLM evaluation framework?
An LLM evaluation framework is the repeatable structure for scoring model and agent behavior: which dimensions you measure, how you score them, what counts as a pass, and how results drive a decision. Converra's framework scores full conversations on goal achievement, sentiment, clarity, relevancy, and safety, then adds use-case metrics and ties every low score to a root cause so the framework outputs an action, not just a number.
What LLM evaluation metrics should I track?
Track outcome metrics (task/goal achievement, escalation correctness, policy adherence), experience metrics (sentiment, clarity, relevancy), and safety metrics (hallucination rate, grounding, prompt-injection resistance). Converra reports these per conversation and per agent, and — critically — head-to-head between a baseline and a candidate fix so you measure real lift rather than an absolute score that an easier scenario mix can inflate.
How is agent evaluation different from model evaluation?
Model evaluation benchmarks a model on fixed tasks in isolation. Agent evaluation measures the deployed system — prompts, tools, routing, and orchestration — on the conversations your users actually have. The same model can pass model evals and still fail as an agent because of a weak prompt or a bad handoff, which is why agent and LLM evaluation are treated as one category on this page.
How is Converra different from eval frameworks like Braintrust or Promptfoo?
Eval frameworks help teams score known test cases they write. Converra uses evaluation as part of an improvement loop: diagnose failures, generate fixes, test candidates head-to-head, and verify production outcomes. The eval surfaces the failure; Converra ships and verifies the fix.
Does Converra support custom metrics?
Yes. Converra evaluates default quality dimensions and use-case-specific metrics such as routing accuracy, lead qualification, escalation correctness, or policy adherence — defined for your agent rather than a generic benchmark.
Can I evaluate my agent without integrating anything?
Yes. The free Converra Eval at /eval probes any production AI agent with adversarial, persona-driven conversations and returns a scorecard with transcripts and prompt-level fixes — no SDK, no signup. It is the fastest way to see LLM agent evaluation applied to your own agent.