A Good AI Agent is a Tested AI Agent

As AI agents become more capable - reasoning, acting, and adapting in real time - they also become harder to predict and control. These are not static systems - they may behave differently every time they're used.

If an agent is to be sustainable - reliable, governable, and safe over time - frequent testing is essential. Before, during and and after development, and when any part of the environment it works within changes.

Learning from TDD

Test-Driven Development (TDD) is a software engineering practice where tests are written before code. The developer defines expected behaviours, writes automated tests for them, then writes just enough code to make those tests pass. The result is code that’s easier to maintain, change, and trust - especially under pressure.

In conventional systems, this means writing unit tests for individual methods, integration tests for services, and regression tests to catch unintended changes.

But what happens when your “code” is just prompts?

No-Code Agents, Real-World Risks

The rise of low-code and no-code tools is accelerating the deployment of AI agents - often by non-developers. Agent Workflow platforms like n8n, Lindy and CrewAI or tool-using LLM wrappers like Claude Code and Goose allow business teams to build agents using flowcharts or natural language.

While these are powerful tools, they introduce risk. They rarely enforce testing discipline. In most cases, there's no structured test suite, no version control, and no testing sandbox.

No-code shouldn’t mean no-tests. If anything, the abstraction layer makes it even more important to validate behaviours in a systematic, repeatable way.

How to Test Agentic AI

Code may be minimal or absent, Language Models may be unpredictable - but AI agents can still be tested. Here are some ways to apply a testing mindset:

🔁 Prompt Replay

Store known inputs (e.g. customer questions) and assert that the agent returns expected types of responses. These can be replayed automatically in CI pipelines or on schedule.

🎯 Goal Achievement Tests

Define clear outcomes and check if the agent achieves them. For example, in a support agent:

• Input: “I want to cancel my subscription.”
• Success: The agent routes the request to the cancellation workflow and confirms action.

📉 Hallucination Benchmarks

Use synthetic and real prompts to test for hallucinations. Compare outputs against trusted ground truth or RAG sources.

🔌 Environment Mocking

Mock APIs, databases, or human responses to see how the agent behaves in known states. This is vital for agents that rely on external tools or plugins.

🧪 No-Code Testing Harnesses

Some no-code platforms now offer basic testing tools: prompt comparison, flow validation, or output snapshots. Teams should integrate these into deployment workflows and track regressions across releases.

Sustainable Means Testable

A Good AI Agent is one that can be maintained, audited, and improved. It's worth reviewing the Good Agents Sustainability Checklist when creating tests. A Good Agent has:

• ✅ Defined behaviours and expected outcomes
• ✅ Repeatable tests for prompts, actions, and flows
• ✅ Monitoring for drift and regression
• ✅ Rollback mechanisms and failure handling

The Path Forward

Test Driven Development methodology needs to adapt. As agents begin to replace structured software we'll no longer be writing classes and methods. Instead we'll be crafting prompts, flows, and policies that drive autonomous behaviours.

But the goal remains: build confidence, reduce risk, and make change safe.

Whether through Python or prompt chains, code or no-code, testing is what separates experiments from "Good" AI agents that are suitable for enterprise use.