I let Codex run for 6 hours. Here’s what happened.

2026-05-27 · Source: How I AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Project & Product Management · Depth: Intermediate, extended

Summary

Codeex's new "goals" feature allows AI models to execute complex, long-running tasks autonomously, moving beyond traditional turn-based prompting. This functionality, detailed in an OpenAI blog post, enables the AI to work towards a defined outcome, iteratively checking its progress and deciding next steps until the goal is met. Key to successful goal setting are measurable outcomes, evidence-based verification, clear constraints, defined boundaries, an iteration policy, and explicit stop conditions. The author successfully used this feature to resolve persistent chat purity errors, eliminate Vercel API errors, clean up approximately 3,900 unread emails in 3 hours and 52 minutes using 6 million tokens, and organize Linear task management. Goals are most effective for durable objectives requiring multiple investigative turns, not simple edits or vague instructions.

Key takeaway

For AI Engineers or Product Managers struggling with repetitive, multi-step tasks, Codeex's "goals" feature offers a powerful solution. If you find yourself constantly prompting an AI for "next steps," define a clear, measurable outcome with verification criteria. This allows the AI to autonomously execute complex, long-running tasks, freeing your time from constant oversight. Consider using it to tackle tech debt, manage backlogs, or automate inbox cleanup.

Key insights

Codeex's "goals" feature enables autonomous, multi-hour AI task execution by defining measurable outcomes and iterative verification.

Principles

• Define goals with measurable outcomes.
• Include evidence-based verification.
• Set clear constraints and boundaries.

Method

Use /goal followed by a prompt detailing outcome, verification, constraints, boundaries, iteration policy, and stop conditions. The AI then loops: work, verify, decide next step, until the goal is met.

In practice

• Automate bug fixes from error logs.
• Clean up large email inboxes.
• Streamline project management tasks.

Topics

• Codeex Goals
• Autonomous AI Agents
• LLM Orchestration
• Software Development Automation
• Task Management
• Email Management

Best for: AI Engineer, Product Manager, Software Engineer

Related on AIssential

• The Codex feature that works while you sleep — Codex's "/goal" feature enables autonomous, long-running AI task execution by defining measurable outcomes and iterative self-correction.
• Ralph-loop 2.0? The real autonomous coder is coming... — LLM-driven goal features enable AI agents to autonomously pursue complex, long-running tasks by continuously evaluating progress.
• Claude Code's New /goal Function Explained — Claude Code's new /goal function enables autonomous, sustained task completion with minimal user intervention.
• The Ultimate Guide to /goal — /goal shifts agent interaction from turn-by-turn prompting to autonomous, goal-driven task assignment with defined completion criteria.
• Codex Goals enable the model to work autonomously for hours without supervision — AI goals enable autonomous, long-term problem-solving for code quality issues without constant supervision.
• Coding agents are giving everyone decision fatigue — Coding agents shift software development bottlenecks from code generation to human judgment, intensifying work and causing decision fatigue.

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Editorial summary, takeaway, and curation by AIssential. Original article published by How I AI.