Agentic Coding: Best Practices for Building Software with AI Agents

Agentic coding is changing how software teams build products. Today’s AI developer tools and AI coding agents can plan implementations, generate production-ready code, run tests, and even interact with external systems. 

But faster development doesn’t automatically mean better software.

Without the right AI coding workflow, coding agents can just as easily accelerate technical debt, architectural mistakes, and maintenance costs. The real advantage comes from combining AI with engineering judgment, clear processes, and strong software fundamentals.

As AI becomes an integral part of software engineering, developers and engineering teams need to rethink not only which tools they use, but how they collaborate with them. Knowing when to rely on automation, when to introduce human review, and how to structure workflows around AI has become a competitive advantage.

This guide shares practical agentic coding best practices we’ve found valuable when building software with AI agents. Because the AI ecosystem changes rapidly, consider these principles rather than fixed rules. The specific tools will change, but disciplined engineering practices will remain essential.

Agentic Coding Best Practices

Plan Mode: Start Every AI Coding Workflow With a Plan

Using plan mode is one of the most effective ways to improve results when working with coding agents. 

Instead of asking an AI agent to immediately generate code, begin by requesting an implementation plan. Once you’ve reviewed and validated that plan, move into implementation.

This becomes the first and cheapest, human quality checkpoint in your workflow.

Reviewing a plain-language implementation plan takes far less effort than reviewing, debugging, or reverting hundreds of lines of generated code. Before approving the plan verify:

• assumptions are correct
• only the necessary files will be modified
• architectural boundaries are respected
• important implementation steps aren’t missing
• the proposed solution actually solves the requested problem

Catching mistakes at the planning stage prevents expensive rework later.

That said, plan mode isn’t necessary for every task.

Simple changes like fixing a typo, renaming a variable, or modifying a single line rarely justify the additional planning overhead. The value of planning grows alongside the complexity, ambiguity, or risk of the task.

Learning to recognize when planning adds value is part of becoming proficient with agentic coding.

There are also several anti-patterns worth avoiding. 

The first is approving plans without reading them carefully, which eliminates the value of the checkpoint altogether. 

The second is accepting plans that are too vague to evaluate, leaving little opportunity to challenge assumptions or improve the solution.

The opposite problem also exists: plans that became so detailed they needed to be rewritten once implementation begins because the AI agent couldn’t possibly know every implementation detail beforehand.

The best plan strikes a balance between specificity and flexibility. It provides enough detail for meaningful review while remaining adaptable once the codebase reveals additional context.

Choose the Right Model for Each Task

Not every large language model excels at the same type of work.

Some models are optimized for reasoning, while others prioritize speed, lower cost, instruction following, or creativity.

Understanding these differences significantly improves the effectiveness of your AI coding workflow. 

Certain models produce stronger implementation plans.

Some perform better during debugging, while others are particularly useful for documentation or refactoring.

Rather than adopting a single model for every task, experienced engineering teams experiment continuously and develop clear guidelines for selecting the most appropriate model depending on the stage of development.

The better you understand each model’s strengths, the more productive your coding agents become.

Manage Context Windows Efficiently

One of the most common mistakes developers make is working inside the same AI conversation for several days.

As context grows, performance often declines.

Responses become less precise, reasoning quality decreases, and token costs increase substantially.

Whenever possible, start fresh conversations instead of continuously extending existing ones.

A clean context allows coding agents to focus on the information that actually matters for the current task.

When previous information still provides value, compacting context is often a better option than carrying the full conversation indefinitely. Long-term memory features can also preserve recurring project information without unnecessarily increasing context size.

Meaning context efficiently is one of the simplest improvements you can make to any AI coding workflow.

Parallelize Development with Git Worktrees

As coding agents become more autonomous, developers spend less time writing individual implementations and more time coordinating multiple streams of work.

While one AI agent implements a feature, another might be fixing bugs, generating documentation, or preparing tests.

Traditional Git workflows, where a single working directory is tied to one active branch, quickly become limiting.

Git Worktrees solve this problem by allowing multiple working directories from the same repository, enabling developers to work across several branches simultaneously.

For teams adopting agentic coding, this unlocks an entirely new level of parallelism.

However, productivity gains don’t come from worktrees alone.

Developers also need the ability to decompose work into independent tasks that can safely execute in parallel.

This requires architectural thinking, not just Git knowledge.

Worktrees become more challenging in complex full-stack systems where multiple branches may require different database migrations, infrastructure configurations, or local environments.

One practical approach is creating initialization scripts that automatically provision isolated local environments whenever a new worktree is created.

Similarly, teardown scripts can clean up those environments once work is complete.

Automating this setup dramatically reduces friction and allows engineering teams to focus on building software rather than maintaining development environments.

Extend Coding Agents with Skills

There are areas where coding agents consistently perform well, and others where they still benefit significantly from additional guidance. That’s where skills become valuable.

Skills are reusable knowledge packages that extend the capabilities of coding agents by providing specialized expertise for specific domains. They can encapsulate best practices for technologies such as React, Ruby on Rails, UX/UI design, testing strategies, security reviews or internal engineering standards. 

One of their biggest advantages is efficient context management.

Unlike adding large documentation files directly into every conversation, skills only consume context when the agent determines they’re relevant or when you explicitly instruct it to use them. This makes them an efficient addition to any AI coding workflow, especially as projects become larger and more complex.

For engineering teams, skills also become a way to package institutional knowledge.

Instead of repeatedly explaining the same architecture decisions, coding conventions, or engineering guidelines, teams can build reusable skills that improve consistency across projects and reduce onboarding time.

Over time, this creates a shared knowledge layer that benefits both human developers and AI agents.

If you’re looking for publicly available skills, repositories such as skills.sh provide a growing ecosystem worth exploring.

However, it’s important to remember that skills should be reviewed with the same level of scrutiny as any external dependency.

Skills are ultimately code.

Before installing one, verify its source, understand what it does, and assess the potential security implications. Treating skills like trusted software libraries is an essential best practice for long-term agentic coding.

Connect AI Developer Tools with MCP Servers

One of the most significant advances in agentic coding has been the emergence of Model Context Protocol (MCP) servers.

MCP servers allow AI developer tools and coding to interact directly with external systems, dramatically expanding what they can accomplish without requiring developers to manually transfer information between applications.

For example:

• A Figma MCP can provide design specifications directly to the agent.
• A Vercel MCP can expose deployment logs for debugging.
• A Jira MCP can retrieve user stories without copying and pasting tickets. 
• Internal documentation platforms can expose architecture decisions or technical standards. 

Instead of constantly switching between applications, developers can allow coding agents to retrieve the information they need directly from the source.

This significantly streamlines the AI coding workflow.

Like every productivity improvement, however, MCP servers introduce trade-offs.

Each external integration typically increases context usage, consumes additional tokens, and may introduce latency depending on the connected service.

Not every workflow benefits equally from every MCP integration.

The goal shouldn’t be connecting every available service.

It should be identifying which integrations genuinely reduce engineering friction while maintaining efficiency and keeping context under control.

Automate Workflows with APIs and CLIs

Not every development platform exposes an MCP server.

Many systems instead provide command-line interfaces (CLIs) or APIs that coding agents can interact with directly. GitHub is one obvious example (at least when I'm writing this article).

While it may not always expose the workflow you need through MCP, its CLI allows agents to automate tasks such as:

• creating pull requests
• generating release notes
• assigning reviewers
• updating issues
• labeling tickets

Other platforms only expose APIs.

Fortunately, coding agents can usually interact with those APIs just as effectively.

Once these integrations become part of your engineering process, repetitive operational work begins to disappear.

Instead of asking developers to manually perform dozens of routine actions, agents can execute them automatically as part of a larger AI coding workflow, allowing engineers to spend more time making architectural and product decisions rather than performing administrative tasks.

Improve Code Quality with Static Analysis

One of the easiest ways to improve the reliability of agentic coding is by surrounding coding agents with deterministic feedback.

AI models generate probabilistic outputs.

Static analysis tools provide certainty.

Linters, type checkers, formatters, and static analyzers immediately identify issues that don’t require human judgment.

Rather than asking an agent: “Check whether there are any type errors.”

Configure tools like:

• TypeScript
• ESLint
• Biome

to run automatically.

The agent can then read the deterministic output, correct the issues, and iterate without requiring developer intervention.

This creates a much more reliable engineering loop.

It also improves AI code review by giving coding agents objective signals they can trust instead of relying exclusively on probabilistic reasoning.

The key principle is simple:

Deterministic systems anchor probabilistic ones.

The more objective feedback your engineering workflow provides, the more reliable your coding agents become.

Let Your Test Suite Drive AI Coding Agents

Automated testing is one of the strongest complements to agentic coding.

When coding agents can execute tests independently, inspect failures, and iterate on their own implementations, they become substantially more autonomous while requiring less human intervention.

The foundation, however, is still the quality of the test suite itself.

Test should be:

• fast
• deterministic
• easy to understand
• descriptive when they fail

Before asking an AI agent to implement a feature, ensure the expected behavior is already captured through tests whenever possible.

Providing clear behavioral expectations dramatically increases implementation accuracy,

Coding agents can also generate automated tests themselves.

This often accelerates development, but it requires careful review.

Poorly designed tests may produce false positives, tests that can pass despite failing to validate the intended behavior.

Engineering teams should also verify that AI-generated changes do not silently weaken or modify existing tests.

Ultimately, automated testing doesn’t replace engineering judgment.

It amplifies it by creating a feedback loop that allows coding agents to continuously validate their own work.

Generate Documentation Continuously

Documentation has always been valuable.

The challenge was rarely understanding its importance and finding time to write it.

Agentic coding changes that equation.

Modern coding agents can generate high-quality documentation in minutes, dramatically reducing the cost of documenting architectural decisions, implementation details, onboarding guides, deployment processes, and internal standards.

Rather than treating documentation as something postponed until the end of a project, engineering teams can now generate and update it continuously as development progresses.

This creates long-term value far beyond helping humans.

Documentation also becomes part of the knowledge ecosystem that future coding agents can leverage, making subsequent development cycles faster, more consistent, and easier to maintain.

Viewed this way, documentation is no longer just a deliverable.

It’s an investment that compounds over time.

Write Better Prompts for Coding Agents

Prompt engineering remains one of the highest-leverage skills when working with coding agents.

Small differences in wording frequently produce dramatically different implementation quality.

Some practices consistently improve results:

• clearly define the expected output
• provide examples whenever possible
• divide complex work into smaller tasks
• eliminate ambiguity
• specify constraints explicitly

Coding agents also respond better to direct instructions than open-ended requests.

For example, instead of writing: “Can you improve this code?”

A much more effective prompt is: “Refactor this function to eliminate the logic between methods X and Y while preserving existing behavior.”

Prompt engineering should not be viewed as a static discipline.

Each generation of models behaves differently.

Strategies that worked well for one model may become less effective for the next.

The best engineering teams continuously experiment, refine their prompts, and adapt their workflows as models evolve.

Automate Repetitive Engineering Loops

As AI becomes more capable, the opportunity isn’t just to automate individual tasks, it’s to automate entire engineering workflows.

Many software development processes follow predictable sequences:

• implement code
• run static analysis
• execute tests
• fix failures
• generate documentation
• prepare pull requests
• update project management tools

Instead of executing these steps manually every time, engineering teams can design reusable loops that allow coding agents to complete much of this workflow autonomously.

Different projects require different loops.

Some teams may prioritize rapid feature development.

Others may focus on reliability, compliance, or infrastructure changes.

The important part is understanding which parts of your engineering process are deterministic enough to automate and which still require human judgment.

Like every other practice discussed in this guide, loop engineering isn’t about removing engineers from the process.

It’s about allowing engineers to spend more time solving complex problems while repetitive execution happens automatically.

As AI coding workflows mature, thoughtfully designed loops will likely become one of the biggest productivity differentiators for software teams.

Don’t Blame AI Before Reviewing the Workflow

When coding agents produce disappointing results, it’s tempting to conclude that AI simply isn’t good enough.

In reality, that’s rarely the root cause.

Poor outputs are often symptoms of deeper issues:

• insufficient context
• ambiguous requirements
• the wrong model for the task
• missing engineering constraints
• inadequate tooling
• weak testing
• ineffective prompts

Before deciding that AI cannot solve a particular problem, evaluate whether you’ve created an environment where success was actually possible.

Ask yourself:

• Did the agent have enough context?
• Were expectations clearly defined?
• Was the appropriate model selected?
• Could deterministic tools have provided better feedback?
• Was the task broken into manageable steps?

More often than not, improving the workflow produces far better results than switching models.

This mindset is one of the most important shifts teams adopting agentic coding can make.

AI doesn’t eliminate the need for engineering judgment. It increases the value of good engineering decisions.

The better your process, architecture, and feedback systems become, the better your coding agents will perform.

Building better systems with IA

Agentic coding is transforming software engineering, but the biggest gains don’t come from simply generating code faster.

They come from building better systems.

Planning before implementation, selecting the right models, managing context efficiently, leveraging AI developer tools, automating feedback loops, and investing in testing and documentation all contribute to a development process that’s faster, more reliable, and easier to scale.

The organizations seeing the greatest impact from AI aren’t necessarily using the most advanced models.

They’re building workflows where AI operates within well-designed engineering practices instead of replacing them.

For founders, CTOs, and engineering leaders, that’s an important distinction.

The challenge isn’t deciding whether to adopt AI coding agents.

The challenge is adopting them without increasing technical debt, sacrificing maintainability, or creating systems that become expensive to evolve.

Ultimately, agentic coding isn’t about replacing software engineers.

It’s about enabling them to make better decisions, deliver higher-quality software, and focus on the work where human judgment creates the greatest value.