Debug with Terminal Coding Agent
This workflow guides developers on using a terminal coding agent to diagnose and suggest fixes for code errors. It involves providing error messages and code snippets to the AI for efficient, in-terminal debugging assistance.
/ quick answer
Utilize a terminal coding agent to quickly analyze error messages and code snippets, providing immediate insights, potential causes, and suggested fixes directly within the command-line interface. This workflow guides developers on using a terminal coding agent to diagnose and suggest fixes for code errors. It involves providing error messages and code snippets to the AI for efficient, in-terminal debugging assistance.
- 01Encounter an error in your terminal while running code.
- 02Copy the full error message and stack trace.
- 03Copy the relevant code snippet where the error occurred.
- 04Invoke the terminal coding agent with a prompt like: `agent debug this error: [paste error/stack trace] in this code: [paste code]`.
- 05Review the agent's explanation of the error and proposed solutions.
- 06Apply the suggested fix(es) to your code, if deemed appropriate and safe.
- 07Re-run the code to verify the fix and iterate with the agent if necessary.
- 08Log the interaction for future reference or for training custom agent behavior.
/ frequently asked
Can the agent fix the code directly?
While the agent can suggest fixes, it's generally recommended for a human to review and apply them, especially in critical systems. Some advanced configurations might allow the agent to apply minor, low-risk fixes automatically after a human 'approve' command, but direct autonomous modification requires high trust and robust validation.
How does the agent handle large codebases or complex systems?
For large codebases, the agent needs context. This can be provided through code snippets, file contents, or descriptions of the system architecture. Agents can also be configured with retrieval-augmented generation (RAG) to access internal knowledge bases or code documentation, helping them understand broader system context and provide more relevant debugging advice.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Coding Agent
A coding agent reads a repository, plans a change, edits files, runs tests and iterates until the task passes — instead of just suggesting snippets.
- →AI Copilot
An in-product AI assistant that helps a user complete a task inside an existing workflow.
- →AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
- →Human-in-the-Loop
A control pattern where humans review high-risk AI decisions before execution.
Related workflows
Turn this into a repeatable process.
- →Run a Coding Agent on a Real Codebase Safely
Give an agent write access without letting it wreck main.
- →Setup Terminal Coding Agent
This workflow outlines the steps to set up and configure an AI coding agent that operates directly within a terminal environment. It enables developers to use AI for coding tasks without leaving their command-line interface.
- →Secure Terminal Agent Execution
This workflow details how to safely execute AI-generated commands and scripts from a terminal coding agent, mitigating risks associated with arbitrary code execution and sensitive data access. It emphasizes a human-in-the-loop approach.
- →Multi-Agent Code Review
This workflow details how AutoGen can orchestrate a team of AI agents to perform autonomous code reviews, identify issues, and suggest improvements, enhancing code quality and accelerating development cycles.
Related tool stacks
The tools that run it in production.
- →Terminal Coding Agent Stack
This stack outlines the essential components for building and operating an AI coding agent directly within a terminal environment. It combines an LLM API with development tools and execution environments.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
Related prompts
Reusable prompts for this job.
- →Operational Anomaly Triage Prompt
Classify alerts and route incidents with evidence and recommended next steps.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
Related use cases
How people apply it, and what came out.
- →Two-Founder Startup Ships Like a Team of Six
Coding agents plus a hard verification gate replaced three planned hires.
Comparisons & alternatives
Pick between the options.
- →Coding Agent vs Autocomplete Copilot
Copilots accelerate typing; coding agents complete tasks. Different unit of work, different review burden.
- →Claude Code vs Cursor: Which Coding Agent to Use
Terminal-native agent vs IDE-native pair programmer — different jobs, both worth using.
- →AI Agent vs Workflow Automation
When to use autonomous reasoning and when to use deterministic automation.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.