AI Agents Testing Services
AI agents are different from AI models. They don't just generate text, they take actions. They call APIs, read and write files, send emails, execute code, and make decisions that have real-world consequences. A bug in an AI agent is not just a wrong answer, it could be a deleted record, a sent email, or a financial transaction.
- 01Agent decision-making accuracy
- 02Tool call reliability and safety
- 03Multi-agent orchestration testing
- 04Boundary and failure conditions
- 05Memory and context retention
AI agents can take real actions, and untested agents can cause real damage
Testing AI agents requires understanding their decision-making process, validating tool call accuracy, ensuring they stay within their defined boundaries, and verifying they fail gracefully when something unexpected happens.
We are one of the first QA teams with dedicated expertise in agentic system testing, building test frameworks, evaluation pipelines, and safety validation processes specifically for autonomous AI systems.
What We Test on AI Agents
Decision Accuracy
- βTask decomposition correctness
- βMulti-step reasoning validation
- βGoal completion rate measurement
- βSub-task sequencing accuracy
Tool Call Reliability
- βCorrect tool selection for each task
- βTool call parameter accuracy
- βTool failure handling and retries
- βUnnecessary tool call detection
Multi-Agent Testing
- βAgent-to-agent communication accuracy
- βOrchestrator and worker agent coordination
- βShared memory and state consistency
- βDeadlock and infinite loop detection
Safety & Boundaries
- βPermission boundary enforcement
- βSensitive data handling validation
- βScope creep detection
- βUnexpected action prevention
Memory & Context
- βLong-context retention accuracy
- βWorking memory utilisation
- βConversation history consistency
- βContext window management
Performance & Cost
- βTask completion latency
- βToken and API cost per task
- βParallel execution efficiency
- βTimeout and cancellation handling
How We Test This Platform
Agent Architecture Review
We analyse your agent architecture, tool catalogue, permission model, and expected task scope to design appropriate test scenarios.
Task Dataset Creation
We create a comprehensive dataset of tasks covering normal use, edge cases, ambiguous inputs, and adversarial scenarios specific to your agent.
Automated Evaluation
We build automated evaluation harnesses that execute tasks, capture agent traces, and score outcomes against defined success criteria.
Safety Red Teaming
We attempt to make the agent exceed its permissions, take unintended actions, or enter dangerous loops, finding safety failures before deployment.
Tool Call Audit
We audit every tool call made during test execution for correctness, necessity, and security, identifying over-permissioned or dangerous tool usage.
Report & Guardrail Recommendations
Detailed failure analysis with specific recommendations for guardrails, permission restrictions, and prompt engineering improvements.
Technology Stack for This Platform
Common Bugs We Catch on AI Agents
Common Questions
Have a specific question?
We're happy to discuss your platform, tech stack, and testing needs in a free 30-min discovery call, no commitment required.
Book a Free QA Strategy CallReAct agents, tool-using agents, multi-agent systems, RAG agents, coding agents, and any autonomous workflow built on AutoGPT, CrewAI, or custom frameworks.
