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. 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
A comprehensive breakdown of every testing area we cover for this platform.
Decision Accuracy
- ✓Task decomposition correctness
- ✓Multi-step reasoning validation
- ✓Goal completion rate measurement
- ✓Sub-task sequencing accuracy
Tool Call Reliability
- ✓Correct tool selection for each task
- ✓Tool call parameter accuracy
- ✓Tool failure handling and retries
- ✓Unnecessary tool call detection
Multi-Agent Testing
- ✓Agent-to-agent communication accuracy
- ✓Orchestrator and worker agent coordination
- ✓Shared memory and state consistency
- ✓Deadlock and infinite loop detection
Safety & Boundaries
- ✓Permission boundary enforcement
- ✓Sensitive data handling validation
- ✓Scope creep detection
- ✓Unexpected action prevention
Memory & Context
- ✓Long-context retention accuracy
- ✓Working memory utilisation
- ✓Conversation history consistency
- ✓Context window management
Performance & Cost
- ✓Task completion latency
- ✓Token and API cost per task
- ✓Parallel execution efficiency
- ✓Timeout and cancellation handling
How We Test This Platform
A structured process with clear deliverables at every stage.
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
We are tool-agnostic. we always select the best technology for your specific needs.
Real Bug Examples We Catch on AI Agents
Real issues we find regularly. bugs that cost businesses money or reputation.
Common Questions
Everything you need to know about how we test this platform.
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 Call →ReAct agents, tool-using agents, multi-agent systems, RAG agents, coding agents, and any autonomous workflow built on LangChain, AutoGPT, CrewAI, or custom frameworks.
Related Platforms
Other platforms we test that are commonly used alongside this one.
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