Practical workflows for improving AI code agent reliability

AI code agents are rapidly moving from novelty to a standard part of the modern development toolkit. While their ability to handle complex tasks is impressive, many teams struggle with their inconsistency. An agent might perform a complex refactor flawlessly one moment and then get stuck in a loop or overwrite important code the next.

This unpredictability often stems from a mismatch between how we prompt the agent and how it technically operates. To get consistent, high-quality results, we need to adopt workflows that account for the agent’s inherent limitations.

This article outlines three practical workflows for working with AI agents to enhance their reliability and make them more predictable development partners.

Workflow 1: Proactive context management

A primary technical constraint for any AI agent is its context window, the finite amount of information it can hold in its working memory. While many LLMs now boast context windows of up to 2M tokens, in reality, their performance degrades near the end of the context window. In a complex codebase, this window can quickly overload, degrading the agent’s performance. It may forget instructions, miss relevant files, or hallucinate incorrect solutions.

An effective workflow actively manages this constraint instead of ignoring it.

• Deconstruct tasks with a plan file: For any significant task, instruct the agent to generate a step-by-step plan. Then, have it save that plan to a file (e.g., refactor-plan.md). Before executing the plan, run a command such as “/clear” to reset the agent’s context. For each subsequent step, start a new session and instruct the agent to reference the plan file. This method breaks an enormous, memory-intensive task into a series of smaller, focused operations, ensuring the agent always has the necessary context without being overloaded;
• Establish a baseline with a project file: Most projects have foundational rules: coding standards, architectural patterns, or key library versions. This information can be stored in a dedicated file (e.g., claude.md) in the project’s root. The agent is configured to ingest this file at the start of every session automatically. This provides a stable baseline understanding of the project’s rules, serving as long-term memory that persists even after the session’s working memory is cleared.

By actively managing the agent’s memory, you shift from hoping it understands to ensuring it has precisely the information it needs for each step of a task.

Workflow 2: Deploying specialised sub-agents

A single, general-purpose agent often struggles to be an expert in all areas of development. A more effective approach is to configure a team of sub-agents, each with a specialised set of instructions for a specific task.

This feature allows you to define different agent personas, such as:

• A code-reviewer agent, with instructions focused on identifying bugs, enforcing style guidelines, and suggesting documentation;
• A debugger agent, pre-configured with knowledge of your logging system and standard diagnostic commands;
• A front-end agent, which has specific instructions about your component library and design system;
• A back-end agent, configured with knowledge of your API standards, database schemas, and preferred frameworks.

When you invoke a specialised agent (e.g., @reviewer), it only loads its specific instructions. This keeps its focus sharp and its context window lean, preventing the prompt from being diluted with irrelevant information.

This approach improves performance by reducing context overhead and enables fine-tuned, reusable instructions for everyday development tasks.

Workflow 3: Implementing a continuous verification loop

Given their autonomy, AI agents can introduce unintended side effects if left unsupervised. A robust workflow incorporates tight, continuous feedback loops to guide the agent and mitigate risks.

• Frequent commits as save points: A simple but powerful technique is to keep a Git client open alongside the agent’s terminal. As the agent completes a small part of a task, commit the changes immediately. This creates a safe recovery point. You can even instruct the agent to make commits itself. If the agent makes a mistake on the next step, you can quickly revert to the last known good state and reissue the prompt with more specific instructions. This process transforms a potentially significant error into a minor, easily managed iteration;
• Automated visual feedback: The most advanced agents can be integrated with external tools. For UI development, integrating with a headless browser tool such as Puppeteer or the new Chrome MCP is particularly valuable. This allows the agent to not only write code but also render the page, take a screenshot, check the console logs, and analyse the visual output to confirm that the changes were implemented correctly. This creates an automated feedback mechanism that verifies the code’s real-world output, not just its syntax.

Effective agent use requires active supervision. By creating tight feedback loops, both manually or automatically with Git and integrated tools, you can guide the agent’s work and harness its power more safely and predictably.