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title: Get Into Agentic Development From the Shallow End
description: A developer describes incrementally building trust with AI coding agents by moving from manual approval of every action to a data-driven permission system....
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# Get Into Agentic Development From the Shallow End

**[Atomic Spin](https://daily.dev/sources/atomicobject)** · 5 min read · 0 upvotes · 0 comments

## Summary

A developer describes incrementally building trust with AI coding agents by moving from manual approval of every action to a data-driven permission system. Starting from a deny/approve/assess model inspired by another engineer's approach to permission fatigue in Claude Code, the author implemented similar rules in Codex using command rules, sandboxing, and guard hooks. To decide which routine operations to safely allow, an observability layer was added that logs approval requests, groups them by intent rather than exact command text, and generates reports ranking candidates for broader permission by frequency and risk. The system only suggests configuration changes; the developer still reviews and manually approves each new rule before adoption, treating autonomy as a gradual feedback loop rather than an all-or-nothing switch.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://spin.atomicobject.com/agentic-development-shallow>

## Questions this post answers

### How can I reduce approval prompt fatigue when using Codex or Claude Code as a coding agent?

Reduce prompt fatigue by implementing a deny/approve/assess permission model: block dangerous operations like recursive deletion or .env access, allow routine operations like local git commands and project-scoped file edits, and continue reviewing everything else. Adding an observability layer that logs and groups approval requests by intent, rather than exact command text, helps identify which recurring prompts are safe to allow permanently.

_daily.dev surfaces more workflows like this for developers tuning agent autonomy without losing control._

### How do you decide which AI agent permission prompts are safe to turn into permanent allow rules?

Group recorded approval requests by intent instead of exact command text, then rank them by frequency, approval rate, and risk level. In one 248-prompt sample, apply_patch writes to a temp directory were approved 98/98 times (100%) and became a top candidate, while GitHub API calls were excluded from broad allow rules despite 100% approval because a generic rule could also permit mutating actions.

_developers automating agent permissions can track these evidence-based rollout patterns on daily.dev._

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---

Tags: [#ai-coding](https://daily.dev/tags/ai-coding), [#observability](https://daily.dev/tags/observability), [#claude-code](https://daily.dev/tags/claude-code), [#openai-codex](https://daily.dev/tags/openai-codex)

[View this post on daily.dev](https://daily.dev/posts/get-into-agentic-development-from-the-shallow-end-qro4g7gz6)

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