Test Your AI Agents Like Production Software
AI agents behave differently from traditional software. Their outputs can vary, tools can fail, workflows can break, and small changes to prompts, models, or data can introduce unexpected regressions. Anbosoft helps teams systematically test and evaluate AI agents before release and throughout production.
We build representative evaluation datasets, define measurable quality criteria, run repeatable evaluations, and create quality gates that help catch failures before your users do.
Evaluation Datasets
- Real-world and edge-case scenarios
- Golden datasets
- Synthetic test data
- Expected outcomes and evaluation criteria
- Production-derived test cases
Agent Behavior & Tool Use
- Task completion
- Instruction and intent adherence
- Tool selection and tool usage
- Multi-step workflow execution
- Response quality and consistency
- Agent-to-agent interactions
Quality, Safety & Reliability
- Hallucination detection
- Content safety
- Output relevance and correctness
- Failure and recovery scenarios
- Regression detection
- Custom business rules and quality thresholds
Build Systematic Quality Gates for Your AI Agents
Testing an AI agent should not be a one-time check before launch. We help you create a repeatable evaluation process that measures agent performance across releases and makes quality visible to your engineering and product teams.
Define agent behavior and quality requirements
Identify what successful agent behavior means for your product, including task completion, accuracy, tool usage, safety, and business-specific requirements.
Build evaluation datasets
Create representative datasets covering normal user journeys, edge cases, failure scenarios, and critical business workflows.
Define evaluators and scoring criteria
Establish measurable evaluation criteria using deterministic checks, model-based evaluators, custom rubrics, and human review where appropriate.
Run agent evaluations
Execute test datasets against your agents and evaluate outputs, actions, tool calls, and multi-step workflows.
Analyze failures and behavioral patterns
Identify where agents fail, behave inconsistently, select incorrect tools, misunderstand intent, or produce low-quality outputs.
Set release thresholds
Define acceptable quality levels and pass/fail criteria so regressions can be detected before deployment.
Automate regression testing
Integrate agent evaluations into your development and CI/CD workflows to continuously compare versions and prevent quality degradation.
AI Agent Testing Built Around Your Product
There is no universal definition of a "good" AI response. The right evaluation strategy depends on what your agent is expected to accomplish.
Anbosoft designs AI agent testing around your product, users, workflows, risks, and business requirements. We combine traditional software QA with AI evaluation techniques to test not only whether the system works, but whether the agent behaves reliably across changing inputs, models, tools, and real-world scenarios.
From conversational assistants and RAG applications to workflow agents, tool-using agents, and multi-agent systems, we help teams build measurable confidence in their AI products before they reach users.