How the Plan-Act-Observe Loop Powers AI Agents in Software Development

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AI agents are autonomous programs that use artificial intelligence to perform tasks such as code reviews, debugging, and optimization without constant human input. At their core, these agents operate on a plan-act-observe loop, a three-stage cycle in which the agent first determines what action to take, then executes it, and finally evaluates the outcome to refine its next move. This iterative feedback mechanism allows AI agents to continuously improve their responses based on real-time results. Developers can build such agents using cloud tools like AWS Lambda combined with relevant SDKs to automate repetitive coding workflows. The approach represents a growing shift in software development toward AI-assisted automation of routine engineering tasks.

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Prompt Engineering Tips for Developers: How Better Prompts Yield Better Code

Many software engineers using AI tools like GitHub Copilot, ChatGPT, or Cursor report inconsistent results, and experts argue the root cause is often poor prompting rather than model limitations. Providing specific context — such as programming language, framework, architecture, and constraints — dramatically improves the relevance and quality of AI-generated code. Treating AI like a new team member rather than a search engine, and briefing it with the same detail you would give a junior developer, leads to more targeted outputs. Assigning the AI a specific role, such as 'Senior Flutter Developer' or 'Security Reviewer', further focuses its responses on the right aspects of a problem. Practical prompt improvements include stating expected versus actual behaviour when debugging, specifying package versions, and clearly outlining project constraints that the AI cannot otherwise infer.

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ProgrammingDEV Community ·

Deterministic Quality Gates Offer More Reliable AI Agent Testing Than LLM Judges

Software developers testing AI agents are being advised to replace or supplement LLM-based evaluations with deterministic quality gates that produce consistent, repeatable results. A quality gate is designed to answer a single, narrow engineering question — such as whether authorization completed before a write tool ran, or whether token usage stayed within budget. Each gate should be deterministic, narrowly scoped, actionable on failure, versioned for change tracking, and portable across local, CI, and fixture-replay environments. The approach recommends normalizing volatile trace fields like timestamps and request IDs before evaluation, and having rules return structured evidence rather than throwing raw assertion errors. Versioning each rule ensures that policy changes are explicitly reviewed, preventing regressions from being mistaken for agent failures.

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ProgrammingDEV Community ·

How Mobile Browsers Silently Break Pointer and Touch Gesture Events

A developer building a custom gesture recognizer for a mobile web app discovered that pointer events behave inconsistently on real mobile devices compared to desktop emulation. Mobile browsers layer pointer events on top of touch events and use heuristics like touch slop to decide whether a touch is a tap, scroll, or zoom, sometimes suppressing or delaying pointer events entirely. On mobile Safari, for instance, the pointerdown event may never fire if the browser suspects an incoming scroll or pinch gesture. Using the CSS touch-action property, rather than relying solely on event.preventDefault(), was found to be a more reliable way to declare which gestures an app handles without breaking native scroll behavior. The investigation, documented with Chrome DevTools examples, highlights how event order, default browser behaviors, and platform-specific quirks are the root causes of most mobile gesture bugs.

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ProgrammingDEV Community ·

How to Debug AI-Generated Code Without Losing Hours to Hidden Bugs

AI coding tools like GitHub Copilot, ChatGPT, and Cursor can generate functions, APIs, and entire features in seconds, but the code often contains subtle errors because these tools lack full context about a project's architecture and business rules. Common issues include calls to deprecated APIs, missing null checks, race conditions, and logic that compiles but fails in production. Developers are advised to treat AI like a junior developer — reviewing generated code critically before running it, rather than copying large blocks blindly. Incremental validation, where code is generated and tested in small components, helps isolate bugs faster than building entire features at once. Checking package compatibility and using structured logging are also highlighted as key strategies for catching AI-introduced errors early.

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