Behaviour evaluation
Measure task completion, trajectory quality and response usefulness.
03 — Practice
Agentic Evaluations & Testing
Evidence for safe, accurate and dependable AI behaviour
Evaluate agent decisions, tool use, grounding and safety across realistic tasks before AI systems operate at production scale.
Agentic Evaluations
Thinknovum Digital Ecosystem
AI assurance
Move agentic systems from compelling demonstrations to dependable operations.
AI agents do more than return text: they interpret goals, select tools, retrieve information and take actions. Traditional software tests cannot fully explain whether those behaviours are accurate, safe or repeatable.
Thinknovum builds evaluation systems around real tasks, expected boundaries and business consequences. Deterministic checks, model-based scoring, human review and adversarial scenarios provide complementary evidence.
Versioned datasets and scorecards make prompt, model, retrieval and orchestration changes comparable, creating a durable quality layer for AI delivery.
Safety boundaries
Probe injection, data exposure and unauthorised actions.
Regression scorecards
Compare quality across model, prompt and workflow versions.
Assurance for autonomous systems
Understand not only what an agent answered, but how it behaved.
Our evaluation model connects product expectations, safety policy and operational limits to repeatable test scenarios. Teams gain a shared basis for improving quality and deciding whether an agent is ready for broader responsibility.
Talk to our expertsTask accuracy
Evaluate completion quality against realistic goals and acceptance criteria.
Tool-use validation
Confirm correct tool selection, parameters, sequencing and recovery.
Safety evaluation
Test policy boundaries, harmful output and adversarial manipulation.
Quality monitoring
Track changes in behaviour with versioned datasets and scorecards.
Solutions and capabilities
Evaluation capabilities for production AI agents
A repeatable system for measuring quality across prompts, models, tools and agent workflows.
01
Task and outcome evaluation
Score whether agents complete goals correctly, efficiently and within defined constraints.
02
Trajectory and reasoning review
Inspect action paths, hand-offs and recovery patterns for avoidable or unsafe decisions.
03
Tool-use validation
Verify selection, permissions, parameters and outputs across connected tools and systems.
04
Hallucination and grounding tests
Measure factual support, retrieval quality and appropriate uncertainty.
05
Adversarial and safety testing
Probe prompt injection, data leakage, policy evasion and harmful actions.
06
Continuous AI regression
Compare releases using stable scenarios, rubrics and quality thresholds.
Business benefits
AI behaviour leaders can evaluate and govern
A measurable evaluation practice reduces uncertainty while supporting faster, more responsible iteration.
Speak to an expert01
Safer agent behaviour
02
Fewer unsupported responses
03
Reliable tool interactions
04
Comparable model decisions
05
Evidence for AI governance
06
Faster controlled iteration
Explore Next-Gen QA
Related capabilities
Build quality into the next release
Put evidence behind every agent release.
Build an evaluation framework matched to your AI use cases, risk profile and operating boundaries.
Discuss your AI evaluation needs