LLM & Agent Evaluation Systems
Make AI Quality Measurable in Auckland
Build repeatable evaluation systems for LLMs, RAG applications and AI agents. XIVTech defines application-specific rubrics, datasets and review loops that turn failures into structured findings engineers can act on.
- LLM output evaluation
- RAG evaluation
- Agent evaluation
- 01Define behavior
- 02Build cases
- 03Run evaluations
- 04Review failures
- 05Regress & improve
Illustrative LLM & agent evaluation systems workflow connecting define behavior, build cases, run evaluations, review failures, regress & improve. Not live results or customer evidence.
Capabilities for LLM & agent evaluation systems
LLM output evaluation
Accuracy, grounding, instruction following and safety checks.
RAG evaluation
Retrieval quality and context-dependent answers.
Agent evaluation
Planning, tool calls, escalation and task completion.
Continuous regression review
Repeatable automated and human checks across changes.
Buyer challenges
What makes LLM & agent evaluation systems work necessary?
Start with the constraints affecting application behaviors, evaluation rubrics and failure categories, then choose the work that addresses them.
Fluency hides failure
Plausible responses can still be unsupported or incorrect.
Agent trajectories are hard to diagnose
Final scores hide planning and tool-use errors.
Changes lack comparable tests
Prompt, retrieval and model updates cannot be assessed against stable criteria.
From the first discussion to reviewed delivery
Shape the engagement around application behaviors, evaluation rubrics and failure categories, with scope and technical responsibilities agreed before implementation.
Clarify the challenge
Review application behaviors, evaluation rubrics and failure categories and the decisions your team needs to make.
Agree the work plan
Identify the first LLM & agent evaluation systems tasks, dependencies and acceptance criteria with your team.
Implement with reviews
Deliver the agreed changes and review representative run evaluations work before expanding adoption.
Validate and hand over
Review evaluation rubrics and the remaining operating responsibilities together.
Working practices throughout delivery
Visible technical progress
Use demonstrations and written updates to review LLM & agent evaluation systems work and surface blockers.
Documented decisions
Record choices affecting application behaviors, evaluation rubrics and failure categories so the team retains the implementation context.
Explicit scope changes
Discuss changed priorities and their delivery implications before proceeding with additional work.
Where LLM & agent evaluation systems fits
Evaluation sits around the complete application behavior, not only its final response. Retrieval, grounding, citations, planning and tool use may each need distinct checks, with automated scoring and human review used where they provide useful evidence.
The service workflow
Define behavior
Select application requirements and quality dimensions.
Build cases
Create representative, edge-case and known-failure datasets.
Run evaluations
Capture model responses and complete agent trajectories.
Review failures
Apply rubrics and classify recurring error patterns.
Regress & improve
Compare changes using repeatable cases and findings.
Application-specific rubrics
Criteria describe useful and unacceptable behavior.
Evaluation harnesses and datasets
Repeatable runs connect cases with results.
Automated and expert review
Checks and human judgment contribute where appropriate.
Useful outputs and practical value
The deliverables follow the agreed LLM & agent evaluation systems scope; priorities are reviewed against your current environment.
Explicit quality criteria
Define the behavior the application needs to get right.
Evaluation rubricsRepeatable test coverage
Capture normal usage and difficult cases.
Evaluation datasets and harnessesActionable failure findings
Connect error categories with engineering decisions.
Regression and failure reports
Ways to work on LLM & agent evaluation systems
Choose a working arrangement around the decision or implementation work needed, rather than a list of tools.
Evaluation pilot & criteria design
Validate quality dimensions and representative cases.
When it fits: The application needs a useful evaluation definition.
Evaluation system implementation
Build agreed datasets, harnesses and review workflows.
When it fits: A defined evaluation initiative needs engineering.
Recurring evaluation collaboration
Continue checks as applications and models evolve.
When it fits: The evaluation backlog changes with the product.
Scope, responsibilities and delivery expectations are agreed before work begins.
Delivery standards you can review
These are LLM & agent evaluation systems delivery practices, not customer endorsements or measured customer outcomes.
- Delivery standard
Review rubric applicability
Check criteria against application-specific behaviors.
Supports: Meaningful quality measurement
- Delivery standard
Retain comparable runs
Version cases and evaluation settings.
Supports: Repeatable regression review
- Delivery standard
Explain failure categories
Record examples and reasoning behind findings.
Supports: Actionable engineering feedback
Keep exploring
Follow the related services, technical context and working practices around LLM & agent evaluation systems.
Related services
Tools and practices
Industry context
Resources and working practices
Questions about LLM & agent evaluation systems
Clarify scope, fit and adoption before choosing the next step.
What can LLM & agent evaluation systems cover?
The offering includes LLM output evaluation, RAG evaluation, agent evaluation, continuous regression review. Discovery determines which capabilities address your priorities and belong in the agreed scope.
Can this work fit our existing environment?
We review application behaviors, evaluation rubrics and failure categories before recommending changes. Existing systems and practices inform the approach and implementation boundaries.
How do we choose the first piece of work?
Validate quality dimensions and representative cases. Discovery identifies the dependencies and acceptance criteria needed to agree the first milestone.
What will our team receive at handoff?
Handoff follows the agreed scope and can include evaluation rubrics, evaluation datasets and harnesses, regression and failure reports. Remaining responsibilities are reviewed with your team.
LLM & Agent Evaluation Systems
Discuss your LLM & agent evaluation systems needs
Tell us about application behaviors, evaluation rubrics and failure categories and the next decision or improvement your team needs to make.