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# I Built an AI Agent to Automate My Daily Tasks: Here’s What Broke (and What Worked)

**[Medium](https://daily.dev/sources/medium_js)** · 4 min read · 0 upvotes · 0 comments

## Summary

A developer shares a first-hand account of building a custom Python-based AI agent to automate daily tasks like email triage, scheduling, and status updates. The initial version failed spectacularly — infinite API loops, hallucinated urgency flags, and unauthorized email replies. After a rewrite, three key fixes made it functional: Human-in-the-Loop (HITL) validation requiring approval before external actions, strict tool scoping with retry limits, and enforced Pydantic/JSON structured outputs. The working agent now saves 1.5–2 hours daily. The core lesson: agent reliability depends on guardrails, state management, and scope definition — not just model capability.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@psaibtech/i-built-an-ai-agent-to-automate-my-daily-tasks-heres-what-broke-and-what-worked-a9974087a821>

## Questions this post answers

### How can I stop my AI agent from getting stuck in an infinite retry loop and burning API credits?

Add hard-coded sanity checks with strict maximum retry limits, such as capping retries at 2 per task, instead of giving the agent open-ended tool access. One builder saw a parsing error cause 45 identical retries in 3 minutes before adding these limits, which stopped runaway execution cycles and uncontrolled API spending.

_Developers hardening agent pipelines against runaway loops follow real-world fixes like this on daily.dev._

### How do I prevent an AI agent from taking unauthorized actions like sending emails on my behalf?

Require human-in-the-loop validation before any external action, such as sending emails, modifying files, or calling APIs. One implementation has the agent prepare a draft and send a Slack or Discord notification with approve or reject buttons, so nothing executes without explicit manual confirmation, preventing incidents like an agent auto-replying to an email without consent.

_Anyone designing safer agent workflows tracks practical guardrail patterns like these on daily.dev._

### Why should AI agents use structured outputs like JSON schemas instead of raw text for tool routing?

Relying on raw LLM text for logical routing is unreliable because outputs can be inconsistent or malformed. Enforcing Pydantic or JSON schemas for every output step means any response that doesn't fit the expected schema gets rejected before the next tool executes, preventing cascading errors from bad agent decisions.

_Developers choosing between text parsing and structured schemas for agent tooling compare approaches on daily.dev._

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---

Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#automation](https://daily.dev/tags/automation)

[View this post on daily.dev](https://daily.dev/posts/i-built-an-ai-agent-to-automate-my-daily-tasks-here-s-what-broke-and-what-worked--fmqywpe8a)

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