Practical Tips for Building AI Agents: boundaries, tools, evaluation
构建 Agent 的技巧要落在检查点上
The useful part of Tips for building AI agents is not every individual click in the recording. The durable lesson is when to use AI agents, how to bound the task, and how to verify the result. This article turns the topic into an executable guide rather than a recap.
The core principle is simple: make the workflow verifiable before making it more autonomous. A long AI-generated answer that cannot be checked creates review debt. A modest intermediate artifact that can be inspected can grow into a reliable system.
先定义失败而不是成功
This pattern fits three kinds of work. The first is work with clear input and a stable output format, such as classifying feedback, editing one component, or creating a migration checklist. The second is tool-assisted work, such as reading files, searching, writing to a CMS, or running tests. The third is collaborative work, where AI prepares a draft and a human approves the next action.
Avoid high-risk automation at the beginning. Deleting data, sending formal messages, changing permissions, creating orders, or making payments should stay behind approval. Boundaries are part of the design, not an afterthought.
工具越多越需要路由规则
- Turn the goal into an inspectable artifact, not an open-ended wish
- List the tools the agent may use and the boundaries it must not cross
- Split the task into observe, decide, act, and review stages
- Leave an intermediate artifact at every stage for human review
- Replay failed cases to verify the workflow can recover
You do not need to automate everything at once. Run one small task, save the successful structure, and reuse the structure next time. The reusable asset is the workflow shape, not a magic prompt.
如何写评估样本
For Tips for building AI agents, a practical loop is: define the goal, gather inputs, restrict tools, and design acceptance checks. If you are assigning a development task, do not write “build this feature.” Write: inspect these files, explain current behavior, change only the target component, run the relevant check, and list remaining risks.
Every stage leaves something observable. Current-behavior analysis shows whether context was understood. A file plan shows whether scope is too broad. A check command catches concrete failures. A risk list tells the human where judgment is still required.
上线后的观测指标
The common failure modes are:
- Treating a chatbot as an agent without tools, state, or acceptance checks
- Granting too much tool access at once
- Reviewing only the final answer while losing plans, evidence, and errors
The fix is usually smaller scope, narrower permissions, and more intermediate checkpoints. Do not use a stronger model as a substitute for workflow design. Stronger models still need boundaries; otherwise they can move confidently in the wrong direction.
下一次迭代怎么做
Try a thirty-minute exercise. Pick one real but low-risk task and write a task card with goal, input, allowed tools, forbidden actions, and acceptance criteria. Save the plan, tool results, final artifact, and failure notes.
Afterward, ask three questions: which step was easiest for AI to misunderstand, which step was hardest to verify, and which step should become a reusable template. Those answers tell you whether to improve the prompt, tool description, or acceptance rule.
下一次迭代怎么做 acceptance checklist
- Can the task be described in one sentence?
- Is the input material complete enough?
- Are missing facts listed instead of guessed?
- Are tool permissions minimal?
- Does every step leave an inspectable artifact?
- Is there a clear recovery point after failure?
- Can a human review the final output quickly?
What to read after 先定义失败而不是成功
This article expands on the topic demonstrated by Anthropic. Source: https://www.youtube.com/watch?v=LP5OCa20Zpg
Use the related articles below to connect this topic with adjacent ideas: concept articles help you choose boundaries, tool articles improve execution, and architecture articles make the workflow production-ready.
Implementation details for 如何写评估样本
In real projects, agents usually fail because feedback is weak, not because the answer is not clever enough. Split the work into small checks: whether sources were read, whether tools actually ran, whether output cites inputs, and whether the final artifact matches the requested format. If these checks exist, occasional model drift can be corrected early.
For teams, maintain a small task library with input examples, expected outputs, allowed tools, forbidden actions, and failure cases. Every time prompts or tools change, replay the library. That improves the system instead of relying on operator intuition.
Reviewing 上线后的观测指标
Do not only ask whether the result is usable. Record whether the input was complete, whether each tool call was necessary, whether failures were recoverable, and how long human review took. Those notes become the improvement path for the next run.
If the same failure appears twice, update the task template, tool description, or verification script instead of relying on memory. That is more stable than switching models reactively.
Operating playbook for 构建 Agent
Turn 上线观测 into a playbook rather than an improvised chat. Write the goal, inputs, allowed tools, forbidden actions, and acceptance checks before execution. During execution, inspect whether the model understood the input, stayed inside boundaries, and can explain each step. If a step cannot be explained, revise the playbook instead of asking for more prose.
The key is turning model output into an inspectable process. Save the task card, source material, tool log, acceptance result, and human notes. Over time, you will see which tasks deserve automation: stable inputs, clear judgment rules, and recoverable failures.
Decision table for 评估样本
Create a three-column table: condition, action, verification. If information is missing, the action is to list missing fields. If production data would change, the action is human approval. If a test fails, preserve the error and return to the previous step. The table turns tacit judgment into explicit rules.
Reusing 上线观测
After the first successful run, save the original input, playbook, intermediate artifacts, verification result, and failure notes. The next similar task should start from this record. For teams, add owner and risk level fields so everyone knows who approves risky actions and which tasks remain semi-automated.