AI agent & MCP testing
EDELLENCE provides AI agent testing, LLM evaluation and MCP testing for systems that retrieve context, select tools, call external services and adapt their behaviour across non-deterministic workflows.
Focused on evidence your product and engineering teams can use.
Quality beyond exact output
Traditional assertions are still useful around APIs, schemas and deterministic boundaries, but they cannot explain whether an agent chose the right plan, used the right evidence or recovered from a failed tool call.
We build evaluation systems around real tasks, expected behaviour and operational constraints. That combines curated datasets, structured rubrics, deterministic contract tests and production signals for quality, latency, cost and reliability.
Service scope
We turn subjective review into repeatable evaluation without pretending probabilistic systems can be reduced to one exact expected string.
Measure whether the system reaches a useful, correct and policy-aligned outcome across representative tasks.
Validate retrieval, evidence use and the relationship between sources and generated output.
Test contracts and behaviour across clients, models, MCP servers, tools and external services.
Track the operational qualities that determine whether an AI feature remains usable after launch.
A few successful conversations do not establish repeatability across tasks, contexts, tools, models and operating conditions.
Teams can compare releases, diagnose failure categories and make changes with evidence instead of intuition alone.
How the engagement works
We adapt the depth and sequence to your product, team and release pressure while keeping priorities and decisions visible.
We identify user tasks, expected outcomes, unacceptable behaviour, dependencies and operational constraints.
We create datasets, rubrics, deterministic assertions and scoring that reflect how the system creates value.
We test retrieval variation, tool selection, MCP contracts, permissions, timeouts and multi-step recovery.
We compare prompt, model, context and tool changes in CI or repeatable release evaluation runs.
Frequently asked questions
Traditional tests remain important for deterministic boundaries. Agent testing adds task-based datasets, behavioural rubrics, statistical comparison and evaluation of planning, context and tool use where exact output is not stable.
MCP testing can cover tool and resource schemas, authentication, client-server compatibility, multi-server routing, tool selection, malformed responses, timeouts, partial failure and recovery behaviour.
Yes. A stable evaluation set makes it possible to compare prompt, model, retrieval and tool changes against the same task and quality criteria, including latency and cost constraints.
We test product-specific safety boundaries, permissions, adversarial cases and failure handling. Highly regulated or specialised model-security assessments may require additional domain experts.
Define quality before scaling
Bring us the task, workflow or MCP integration that is difficult to trust. We will help define, evaluate and automate the signals that matter.