{"slug": "asking-vs-delegating-ai-agents", "title": "Asking vs Delegating AI Agents 🧐", "summary": "A developer argues that most engineers underutilize AI by treating it as a smarter Stack Overflow, asking questions instead of delegating tasks. The post outlines a shift to delegating work to AI agents with clear, verifiable instructions, demonstrating how this approach saves time on tasks like debugging, refactoring, and database migrations. It also provides a framework for reviewing agent output to ensure correctness and codebase fit.", "body_md": "Most developers use AI like a **smarter Stack Overflow**.\n\nType a question. Get an answer. Go do the work **yourself**.\n\nThat's fine but it's the slow way 😩 There's a faster mode, and most people haven't switched to it yet.\n\nWhen you ask an **AI**:\n\n\"How do I write tests for my auth module?\"\n\nYou get a nice explanation. Then you write the tests yourself. You're still doing the work 🥸\n\nWhen you **delegate** to an AI agent:\n\n\"Write tests for\n\n`/src/auth.py`\n\n. Cover login, logout, and invalid token cases. Run them. If any fail, fix the code until they pass. Tell me what you changed.\"\n\nThe agent `opens`\n\nyour files, `writes`\n\nthe tests, `runs`\n\nthem, `reads`\n\nthe failures, `fixes`\n\nthe code, and `comes back`\n\nto you with a working test suite.\n\nYou review the result. You didn't do the work.\n\nThat's the shift 🙂↔️ It sounds small. The `time difference is huge`\n\n.\n\nEvery delegation that works has **four parts**. Think of it like giving a task to a new team member:\n\nHere's what that looks like in practice:\n\n**Debugging:**\n\n\"Here's the error and the stack trace. Find the root cause, fix it, and explain what was broken.\"\n\n**Why this works:** You're not asking what the error means. You're `handing over`\n\nthe whole problem, find it, `fix`\n\nit, `explain`\n\nit 😎\n\n**Refactoring:**\n\n\"Refactor this file. Max two levels of nesting. No single function longer than 30 lines. Update every call site in the codebase.\"\n\n**Why this works:** The constraints are `clear and checkable`\n\n. The agent knows exactly when it's done 🧐\n\n**Database migration:**\n\n\"Write a migration script for this schema change. Make it idempotent. Run it against a local test database and\n\n`confirm`\n\nit succeeds.\"\n\n**Why this works:** You gave it a way to `verify its own work`\n\nbefore coming back to you 🤔\n\n**PR review:**\n\n\"Read this PR diff. Find anything that could fail in production. Write the tests I missed.\"\n\n**Why this works:** Two goals, both specific. The agent can do both end to end 🥵\n\n**Data pipeline:**\n\n\"This pipeline has no tests.\n\n`Write a test`\n\nsuite that checks: schema consistency at each step, no data leakage between train and validation sets, and correct handling of null values.\"\n\n**Why this works:** A boring job that always gets skipped. Now it's a 5-minute task 🥱\n\n**Before you open your IDE:**\n\n\"Here's the Jira ticket. Find the relevant files in the repo. Give me a summary of what needs to change before I write a single line.\"\n\n**Why this works:** You start coding with context instead of spending 20 minutes doing archaeology 🥱\n\nAgents are fast. They're also wrong sometimes, not randomly, but in predictable ways. Here's how to catch it without spending 40 minutes re-reviewing everything.\n\n**Check 1:** Did it actually solve the problem?\n\n`Run the code`\n\n. Don't just read it.\n\nAgents can write code that looks completely correct and `fails in a specific edge case`\n\n. The only way to `find out is to execute it`\n\n. If there are tests, run them. If there aren't, run the thing manually. Or let the agent write the test code.\n\nReading code feels like reviewing. **Actually running it is reviewing**.\n\n**Check 2:** Does it fit your codebase?\n\nThe agent doesn't know your `team's conventions`\n\n. It doesn't know why that module is structured that way, or that you decided last quarter never to use that pattern again. Technically correct code `can still be wrong`\n\nfor your situation.\n\nScan the output for anything that feels off, an unusual pattern, a library you don't use, an approach that doesn't match how the rest of the codebase works. That's the thing `only you can catch`\n\n.\n\n**Check 3:** Did it change anything you didn't ask about?\n\nCheck which files it touched. `Read the diff`\n\nlike you'd read a PR from a junior developer.\n\nAgents sometimes make `helpful`\n\nchanges, cleaning up something nearby, refactoring a function that wasn't in scope. Sometimes `that's great`\n\n. Sometimes `it breaks`\n\nsomething else. Know what it touched before you accept it.\n\n**The evaluation matrix**\n\nThe mindset shift in one sentence\n\n| What to check | The question to ask | Red flag |\n|---|---|---|\n| Goal | Did it actually solve the problem? | Tests pass but logic is wrong |\n| Fit | Would your team merge this as-is? | Right code, wrong conventions |\n| Scope | Did it only touch what you asked? | Changed files you didn't mention |\n\nThat review takes 5–10 minutes on most tasks. Not 40.\n\nYour job changes **from doing the work to defining the goal and reviewing the result**.\n\nYou bring **judgment and context**. The agent brings **speed and tirelessness.**\n\n**What do you think? 🤔 Drop it in the comments**", "url": "https://wpnews.pro/news/asking-vs-delegating-ai-agents", "canonical_source": "https://dev.to/omerberatsezer/asking-vs-delegating-agents-43ii", "published_at": "2026-06-26 12:59:52+00:00", "updated_at": "2026-06-26 13:04:36.425864+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "developer-tools", "large-language-models", "ai-products"], "entities": ["Stack Overflow", "Jira"], "alternates": {"html": "https://wpnews.pro/news/asking-vs-delegating-ai-agents", "markdown": "https://wpnews.pro/news/asking-vs-delegating-ai-agents.md", "text": "https://wpnews.pro/news/asking-vs-delegating-ai-agents.txt", "jsonld": "https://wpnews.pro/news/asking-vs-delegating-ai-agents.jsonld"}}