AI Agents - HelpMeTest

AI Agents

Testing LangGraph State Machine Agents: Nodes, Edges, State Transitions, and Checkpointer Mocking

LangGraph compiles agent behavior into a directed graph. Nodes are functions. Edges are transitions. State flows through the graph, getting modified at each node. It's a clean mental model — and it creates a natural seam for testing. The problem is that most LangGraph developers test the graph as

AI Agents

Testing CrewAI Agent Crews: Task Delegation, Role Validation, Tool Assertions, and Output Validation

CrewAI orchestrates multiple agents working together toward a shared goal. A researcher agent gathers information. A writer agent drafts content. An editor agent refines it. Each has a role, a set of tools, and a defined responsibility. The complexity that makes CrewAI powerful — multi-agent coordination, role-based delegation, sequential task chains

AI Agents

Testing AI Safety Guardrails: Input/Output Filter Validation, PII Detection, and Adversarial Jailbreak Testing

AI guardrails are the safety layer between user input and your language model. They block prompt injections, filter inappropriate content, redact PII from outputs, and resist jailbreak attempts. They're also the part of your AI system that's most likely to be skipped in testing. The reasoning

AI Agents

Testing AI Agent Observability: LangSmith Integration Tests, Trace Validation, and Cost Regression Testing

Observability for AI agents is different from observability for traditional software. A web server trace shows function calls and database queries. An AI agent trace shows LLM calls, tool invocations, token counts, and reasoning chains. The failure modes are different too: not just errors and timeouts, but hallucinations, reasoning loops,

AI Agents

End-to-End Testing of Multi-Agent Pipelines: Deterministic Replay, Agent Mocks, and Pipeline Assertions

A multi-agent pipeline is a chain of AI agents where each agent's output becomes the next agent's input. Agent A researches. Agent B synthesizes. Agent C writes. Agent D edits. When the final output is wrong, you have a debugging problem: which agent introduced the failure?

LLM Testing

AgentOps for AI Testing: Session Replay and Agent Observability

Testing an AI agent is not like testing a traditional API. When a REST endpoint misbehaves, you read the logs and find the bad request. When an AI agent misbehaves, you're staring at a black box that made a sequence of decisions — each one plausible in isolation, wrong

AI Testing

Testing AI Agents Built with Google ADK

Google's Agent Development Kit (ADK) makes it fast to build production-grade AI agents. Within a few hundred lines of Python, you have a multi-agent system with tool use, session management, and streaming responses. What ADK doesn't give you is a testing framework. That's your