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Across the last few months, devs have been reporting the same frustrations. Coding agents are still treated like slot machines. Insert a general prompt, execute the output, and anticipate it will function as intended. That pattern keeps showing cracks. It ultimately reduces to these 3 issues.

Unclear prompts: Vague prompts like “make this cleaner” don’t give models enough direction.

Lack of structure: Without a clear plan, engineers burn time on cleanup instead of real progress.

Overwhelming context dumps: Dropping entire repositories or oversized histories into prompts slows models and causes regressions.

So how to fix it?

Progress is more consistent when AI coding sessions are structured like pair programming. The shift happens by applying three habits: planning, targeted context and memory.

Start with a plan

Before writing code, define the single objective, what “done” looks like, and the boundaries you cannot break.

## Plan

Objective:
- [Specific technical goal in one sentence]

Success Criteria:
- [Measurable outcome 1]
- [Measurable outcome 2] 

Constraints:
- [Technical limitation 1]
- [Integration requirement 1]
- [Performance/security requirement 1]

Engineer the context

Precision beats bulk. Instead of pointing to the entire codebase, select the key files, functions, and domain rules that matter.

## Targeted context

Files to Include:
- [file-path] (specific line numbers if relevant)
- [related-file] (reference for context)
- [new-file] (if creating something new)

Domain Notes:
- [Business logic constraint 1]
- [Technical dependency 1]
- [Integration requirement 1]

Security/Performance Constraints:
- [Security requirement]
- [Performance requirement]
- [Logging/monitoring requirement]

Track by updating agent memory

Models forget across longer sessions, so progress needs a lightweight log. A simple progress.md works well

## Overview
- Purpose: [goal]
- Stack: [tech details]

# Status
- [x] Done: <task>
- [ ] Current: <task>

# Task list
- [task] — [file:lines] — [goal]

# Notes
- [commands, style rules, references]

This file gives both the developer and the agent a common thread, preventing repetition and lost steps across multiple chat sessions You can read how to implement it below

Recent developer posts across Reddit, GitHub, and industry blogs point to the same conclusion: coding agents only save time when they’re used within a clear workflow anchored by planning, precise context and agent memory.

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