AI Engineering Now #003: Your AI Agent Needs a Test Before More Tools — Why Agent Evaluation Matters

AI Engineering Now #003

11 November 2026 | AI Agent Evaluation

Your AI Agent Needs a Test Before It Needs More Tools

NOW

AI agents are becoming increasingly capable of using tools, retrieving information, writing code, and executing multi-step workflows.

But an agent successfully calling a tool does not mean it has successfully completed the task.

OpenAI's Agent Evals guidance explains how developers can evaluate agent performance using traces, graders, and repeatable evaluation datasets.

WHY IT MATTERS

An agent may execute every tool call correctly and still deliver the wrong result.

For AI engineers, the important question is not:

Did the agent use its tools?

It is:

Did the agent accomplish the intended task correctly?

Reliable AI Engineering requires evaluating outcomes, not just execution activity.

TRY THIS

Create a small evaluation dataset containing 10 realistic tasks for your AI agent.

For each task, record:

1. Expected result: What should the agent accomplish?

2. Actual result: What did it produce?

3. Tool errors: Did any tool calls fail?

4. Task success: Were the requirements satisfied?

Run the same 10 tasks after changing your model, prompts, or tools.

Compare the results before adding more capabilities.

ENGINEERING TAKEAWAY

Don't measure how busy your agent is. Measure how well it accomplishes the task.

SOURCE — OpenAI Developer Documentation

Agent Evals

https://developers.openai.com/api/docs/guides/agent-evals

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AI Engineering Now #002: Your AI Agent Needs an Exit Condition — When Should an AI Agent Stop?

AI Engineering Now #002

11 November 2026

Your AI Agent Needs an Exit Condition

NOW

AI agents can execute multiple steps, call tools, and continue working toward a goal.

The OpenAI Agents SDK provides a max_turns setting to limit the number of agent turns during a run.

But limiting turns is only one part of controlling autonomous execution.

WHY IT MATTERS

An agent that keeps working is not necessarily making progress.

Without clear boundaries, an agent may repeat failed actions, consume unnecessary resources, or continue when human intervention is needed.

TRY THIS

Before deploying your next agent, define three boundaries:

1. Turn limit: How many agent turns are permitted?

2. Resource budget: How much time or money can the task consume?

3. Escalation rule: When must the agent stop and ask a human?

Test what happens when each boundary is reached.

ENGINEERING TAKEAWAY

An effective AI agent knows not only what to do, but when to stop.

SOURCE — OpenAI Agents SDK Documentation

https://openai.github.io/openai-agents-python/running_agents/

Technical note: The SDK documents max_turns. Time, cost, and escalation policies require additional application-level controls.

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AI Engineering Now
One development. One insight. One action.

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AI Engineering Now #004: Context Engineering — Why Better Context Matters More Than a Better AI Model

AI Engineering Now #004 Context Engineering

Your AI Agent May Not Need a Better Model. It May Need Better Context.

An AI agent can have a powerful model, capable tools, and well-written instructions—and still produce unreliable results.

The missing ingredient may be the right information at the right moment.

WHY IT MATTERS

More context is not automatically better context.

Long conversation histories, irrelevant retrieved documents, and outdated tool outputs can make it harder for an agent to identify what matters.

Context engineering is about designing what information the model receives, when it receives it, and how that information is organized.

TRY THIS

Take one AI agent task and test three configurations:

1. Full context: Complete conversation history and all retrieved documents.

2. Focused context: Only the most relevant documents and current task instructions.

3. Structured context: Relevant documents, a concise task summary, and explicit success criteria.

Compare answer accuracy, task completion time, and token consumption.

ENGINEERING TAKEAWAY

Don't just give your agent more information. Engineer the information it needs to succeed.

SOURCE — Anthropic Engineering

Effective Context Engineering for AI Agents

https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents

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AI Engineering Now
One development. One insight. One action.

AIYING — Accomplishing with AI

#AIEngineering #ContextEngineering #AIAgents #AgenticAI #LLMOps #GenerativeAI #AIYING