AI agent evaluation by domain experts
Know whether your chatbot, copilot or agent actually does the job. Shaip’s subject-matter experts score single-turn answers, multi-turn conversations and full agent trajectories against rubrics we build with you — and return findings your team can act on.
What is AI agent evaluation?
AI agent evaluation is the measurement of how well an LLM-powered chatbot, copilot or autonomous agent performs real tasks — not just how a single answer reads, but whether the agent retains context across turns, calls the right tools in the right order, and completes the user’s goal correctly and safely.
Shaip delivers this as expert agent evaluation: domain specialists score single-turn responses, multi-turn conversations and full agent trajectories against a rubric we design with you, then return scores, rationales, error taxonomies and the findings your team needs to fix the agent.
From AI risk to reliable agents
Why AI agents fail in production without expert evaluation
Getting an agent to demo is easy. Trusting it in production is the hard part — and automated checks alone don’t close the gap. These are the failures that stall enterprise rollout, and what expert AI agent evaluation is built to catch.
Hallucinations & wrong answers
Agents state false or unsupported claims with confidence, and generic benchmarks miss the errors specific to your domain.
Unsafe or off-policy actions
Toxic, non-compliant or off-brand responses slip past automated review and reach real users.
Tool & multi-turn failures
The wrong tool, wrong arguments, or context lost across turns — failure modes automated checks rarely catch.
Slow, costly validation
Months of manual review per release, and ongoing rework that drains engineering time.
What we evaluate
What we evaluate in your AI agent
From a single response to a full multi-turn trajectory, Shaip’s experts score every dimension of agent performance that automated agent testing can’t judge on its own.
💬 Single-turn response quality
- Helpfulness & correctness
- Completeness & instruction-following
- Tone & brevity for the scenario
- 1–5 or 1–7 rubric scores with rationales
🔁 Multi-turn conversation
- Context retention across turns
- Coherence & recovery from ambiguity
- Persona & policy consistency
- Per-turn and whole-conversation scores
🛠️ Tool use & trajectory
- Right tool, right arguments, right order
- Were tool results used correctly
- Step-level correctness scoring
- Error taxonomy & corrected trajectory
🎯 Task completion
- Goal reached within constraints
- Partial credit for recoverable errors
- Pass/fail with graded partial credit
- Time-to-completion notes
✅ Factuality & grounding
- Claims verified against sources
- Hallucination & citation accuracy (RAG)
- Source faithfulness & grounding checks
- Claim-level verdicts & unsupported-claim rate
⚖️ Preference & comparison
- Which of two+ responses serves the user better
- Pairwise & ranked comparison
- Win rates with written rationales
- Preference labels usable for RLHF/DPO
📄 Long-context handling
- Retrieval of the right detail from long inputs
- Omissions & distortions flagged
- Coverage & accuracy scores per document
How we score
How we score your AI agent
We pick the agent evaluation method that fits your decision — a release gate, a model-vs-model comparison, or continuous production monitoring.
Rubric-based scoring
Absolute scores against defined criteria — the backbone of release gates and regression tracking.
Pairwise & ranked comparison
Head-to-head between agent versions, prompts or models; produces RLHF/DPO preference data.
Task-based pass/fail
Run the agent against defined tasks, from your logs or a sandbox; pass/fail with graded partial credit.
Gold-trajectory comparison
Score the agent's steps against an expert-written ideal trajectory.
LLM-as-a-judge + human calibration
Automated screening at scale, validated against an expert human baseline.
Custom protocol
Long-context review, interactive probing, or any protocol your team specifies.
Who scores your outputs
Your AI agent, scored by domain experts
Evaluators are matched to the domain of the conversation. Every evaluator passes a domain test and a calibration set before scoring a single production item.
Customer-support leads
Experienced support leads score customer-service and support agents for accuracy, tone and escalation.
Software engineers
Developers score coding copilots for correctness, security, tool use and code style.
Physicians & clinicians
Clinical assistants and patient-facing agents scored for medical accuracy, safety and guideline adherence.
Financial analysts
Financial assistants scored for numerical correctness, compliance and regulatory language.
How it works
How an AI agent evaluation project runs
A managed, end-to-end workflow from your sample conversations to pipeline-ready findings.
Share & scope
Send sample conversations, your policy or persona spec, and the outcomes you care about.
Rubric & taxonomy
Shaip drafts the rubric, error taxonomy and scoring guide; you approve within a week.
Calibrate
Experts calibrate on 50–100 items; agreement is checked before production.
Score with QA
Production scoring with blind double-scoring on a sample and weekly reports.
Deliver
JSONL or CSV with scores, rationales, taxonomies and a findings summary.
What you get
What your AI agent evaluation delivers
Not a dashboard you have to interpret — finished findings and training-ready data.
Scores & rationales
Scores per item, per turn and per trajectory, each with a written rationale.
Error taxonomy
The recurring failure modes, ranked by frequency and severity.
Preference data
Pairwise preferences and rankings ready for RLHF, DPO or SFT where comparisons were run.
Corrected responses
Expert-written ideal or corrected responses wherever you ask for them.
Findings deck
Where the agent fails, why, and what data or prompt changes would fix it.
Pipeline-ready formats
JSONL, CSV or direct delivery into your platform via API.
Why teams choose Shaip
Data Collection Capabilities
Create, curate, and collect custom-built datasets (text, speech, image, video) from across the globe based on custom guidelines.
Flexible Global Workforce
Leverage 10,000+In-house global workforce and 500K+ Crowd-scale credentialed contributors. Real-time workforce capacity and efficiency.
Quality
Our proprietary platform & skilled workforce use multiple quality control methods to meet or exceed quality standards.
Diverse, Accurate & Fast
Our process streamlines, the collection process through easier task distribution, & data capture directly from the app & web.
Data Security
Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.
End-to-End Annotation
Every collected dataset can be annotated, labeled, transcribed, and validated in the same workflow, delivering model-ready training data.
Security & Compliance
Scope an AI Agent Evaluation
Send a sample of conversations and the outcomes you care about. We'll return a rubric and an expert-scored pilot you can act on — managed end to end, delivered in the format your pipeline expects.
Contact UsFrequently Asked Questions (FAQ)
1. What is AI agent evaluation?
AI agent evaluation measures how well an LLM-powered chatbot, copilot or autonomous agent performs real tasks — context retention across turns, correct tool use, and safe, correct task completion — not just how a single answer reads. Shaip runs it as a managed service with domain experts who score against a rubric built with you.
2. Why aren’t benchmarks and evaluation frameworks enough for agentic AI?
Public benchmarks tell you how a model ranks on standardised tasks, and frameworks let you automate checks on your traces — but neither judges whether your agent did the right thing for your customer, in your domain, with your tools. That judgment depends on your policies and needs qualified human scoring, which is the layer Shaip delivers.
3. How do you evaluate AI agents for enterprise and customer-service use?
We match evaluators to the conversation’s domain — support leads for customer-service agents, engineers for coding copilots, physicians for clinical assistants, analysts for financial assistants — and score single-turn answers, multi-turn conversations, tool use, trajectories and task completion against a calibrated rubric.
4. Can you evaluate agents that call our internal tools?
Yes. We work from trajectory logs or a sandbox you provide; experts score each step against the expected behaviour, including whether the right tool was called with the right arguments and whether its results were used correctly.
5. Do you also use automated evaluation and LLM-as-a-judge?
Yes. For high volumes, automated metrics screen every output for relevancy, hallucination and toxicity; experts then review flagged and sampled items and score what automation can’t measure. See LLM-as-a-Judge Calibration for how the automated and human layers are kept in step.
6. Do you produce data we can train on?
Yes. Pairwise preferences, rankings and corrected responses are delivered in formats usable for RLHF, DPO or SFT, so an evaluation feeds directly into your next round of improvement.
7. How many conversations can you handle?
From a few hundred for a release gate to hundreds of thousands for continuous evaluation, using Ubiquity’s global delivery capacity.
8. Can you evaluate voice agents and in languages other than English?
Yes. Native-speaker evaluators score voice and chat agents in 50+ languages, including transcript accuracy and conversation quality.
