What is agentic coding? Benefits, risks, and best tools to consider

"Agentic coding is the future!" But what does that mean exactly? And more importantly: what does it mean for your team?

Agentic coding can be an incredible way to speed up code production on your team and remove grunt work from your senior engineers. But as your code base grows, you need to have security measures in place to review that code.

In this article, we'll break down the benefits, risks, and best practices to help make agentic coding a smoother part of your workflow, and ship better code, not just more of it.

What is agentic coding?

Agentic coding is a software development process where autonomous AI agents plan, write, test, and modify code with minimal human involvement.

AI agents can work toward multi-step goals or outcomes without needing step-by-step instructions. Instead, they're able to break down bigger or vague goals or tasks into smaller steps and can execute on those independently.

What is agentic coding vs. AI coding?

Agentic coding uses independent, autonomous agents to execute high-level instructions. AI coding uses AI tools to generate text or code in response to specific user questions, or to help autocomplete within existing codebases.

AI coding and agentic coding aren't opposites so much as a continuation down a spectrum of AI-assisted development. Here are some core differences:

What is agentic coding vs. vibe coding?

Agentic coding uses independent, autonomous agents to execute high-level instructions. Vibe coding is focused on experimentation, intuition, and creative exploration. In general, vibe coding is often more about prototyping and exploration, while agentic coding is more suited for production-ready code, complex codebases, and structured, well-engineered projects.

On the spectrum of AI-assisted software development, vibe coding is on one end, and agentic coding on the other. Engineering leaders are shifting to agentic coding, while vibe coding is more popular among non-technical builders. One of the keys that makes agentic coding work for engineering leaders is setting it up properly: giving your agent context, pipeline (an independent verification layer), and most importantly, oversight. Vibe coding often lacks oversight as non-technical builders allow the agent to make major decisions (about the code and architecture) and execute them.

We'll get into this more below. For now, here's a breakdown of some key differences:

How does agentic coding work?

Many engineering teams are already familiar with various forms of AI coding, typically involving autocomplete embedded in the IDE or AI assistance via prompts (back-and-forth, one question or task at a time).

Agentic AI works at a higher level. For example, an agentic AI workflow might look like:

• Assign a bug fix to your agentic AI (or have your agent find issues related to your pull requests on its own, from Linear, for example).
• The agent reads and analyzes the bug.
• The agent searches your codebase to locate the error.
• It autonomously makes a plan for how it should be fixed and the expected outcome.
• It then writes the patch and applies the fix to the code.
• The agent then runs the necessary tests, or better yet, another agent (one that didn't just write the code), such as Greptile, runs the test and proposes fixes if there are any errors found.
• Long-running agents continue to iterate and test until no errors are found and code works as expected. This can take hours, days, or even weeks and the agent will continue until all milestones are complete. (Alternatively, a long-running agent can stay in "sleep mode" for long periods of time until an event or task trigger activates, at which point, it begins to run workflows and tasks autonomously.)
• The agent then files a PR with the updated code for human review.

This is a far cry from the standard AI coding process, where a developer would need to go back and forth with the AI between each step, providing the processes, next steps, and adjustments.

In short, agentic engineering goes beyond "writing code with AI" to agents actually navigating your work, migrations, codebases, problems, and architecture with context, autonomy, and collaboration with your engineering team.

For example, an agentic migration of a legacy codebase could look like hundreds of agents running in parallel, each working against its own copy of the codebase, each attempting a task, checking its work, and moving to the next task.

Your engineering role becomes deciding where an agent runs on its own and where a script directs it to stop, where an agent can decide and where it should only execute.

AI agents have four key elements beyond standard AI coding tools:

• Context: AI agents are able to make decisions and create processes that take into account your entire codebase: surrounding files, dependencies, frameworks, etc. Instead of generating generic code based on a single prompt and perhaps some inputted context, agents can review your entire codebase and development environment to reduce errors and improve code quality.

  For example: Greptile's AI code review agent uses a self-updating knowledge base that documents each part of your code and how it connects alongside past bugs and fixes. You can trust that your agent's context is complete and updated, and you don't need to spend time creating complex "context" explaining your codebase to an AI tool.

• Autonomy: Agents are able to make decisions, problem-solve, test, and create plans on their own. Given high-level instructions, they can complete the full autonomous loop, all with very minimal human intervention throughout.

• Complexity: Agentic AI uses external memory structures to create more reliable working and episodic memory. Rather than relying on a single context window (as with standard AI coding tools), agentic workflows are able to store, organize, and retrieve context, decisions, interactions, outputs, and so on for reference later. This creates a more reliable and stable agent that can "remember" how you've accomplished things in the past.

• Collaboration: Agents are able to work with and "oversee" other agents to complete work independently. For example, greptile review --agent lets agents use the Greptile CLI to review code independently. The agent writes, Greptile independently reviews and then provides raw text with a suggested fix for your agent to apply. Adding greplooping as a skill makes it easy for your coding agent to review independently, catch and fix more bugs, and complete the full autonomous loop.

Agentic coding benefits

Agentic AI doesn't (shouldn't) replace human engineering and experience, but it can provide some major benefits for your team:

• Improved efficiency. Instead of having senior devs writing boilerplate, fixing bugs, or building basic features, agents work independently. You ship features faster because humans provide the architecture, oversight, and higher-order thinking, while agents do the grunt work.
• Workflow automation. Why spend time doing the same simple tasks over and over when you can build a workflow for them once and let an agent complete them going forward?
• Focus on higher-value tasks. Your engineers have the experience, intuition, and creativity that agents don't. Put your senior devs in charge of architecture, workflow engineering, and improving the codebase structure and functionality on a high-level. Agents work on writing syntax and fixing bugs.
• Improve security reviews. By using multiple agents working together to review bugs, run security testing, and test code, your senior devs get higher signal:noise comments from agents and you get to ship better code, the first time.

Agentic coding risks

Agentic AI tools can be a massive force multiplier, but they need to be deployed thoughtfully, especially in an enterprise context.

• Possible vulnerabilities. One of the biggest risks is simply that letting agents act autonomously introduces the possibility of many types of security risks and vulnerabilities: insecure code, misconfigured permissions, exposed user data, excessive agency. The key is controlling for these vulnerabilities early and often. For example: running agent-generated code through sandbox testing to catch errors before they hit prod.
• Weak logic. Agentic code can often suffer from weak logic from a lack of context (or too much irrelevant context), limited memory windows, or other agentic limitations. You'll need to control for this to ensure that new, agentic code doesn't break existing code, creates a clean codebase, and so on.
• Compliance gaps. Agentic code introduces the possibility of security and compliance gaps as well as vulnerabilities. When you don't have a human who can verify they wrote the code compliantly, you need additional layers of review to ensure compliance before approving PRs. AI security reviews can help with this.
• Endless review loops. A common risk of agentic coding is the agent getting stuck in a review loop: finding an error, fixing it, creating another error, fixing that, finding and fixing errors endlessly, even if the "errors" aren't critical. Multiple agents working on reviews and strict review parameters (closed number of review cycles, ranked comments, etc.) can cut back on this tendency.

Best agentic coding tools

If you're looking for the best options for agentic AI coding, start here:

Codex: Best for heavy code writing

Codex (by OpenAI) is often referred to as the "workhorse" of agentic AI. It excels at writing code and handling complex projects, often reviewing and fixing code and suggesting improvements proactively. It is great at sticking to documentation, but tends to be over-active in suggesting changes or reviewing (e.g. marking low-priority fixes as high priority, leaving too many comments).

According to our research, Codex tends to produce more errors (compared to human-generated PRs) around configuration and breakage: env-var / config bugs (1.35× human-code), "breaks existing" (1.34×), and "secret in logs" (1.34×).

Claude Code: Best for planning and high-level architecture support

Claude Code offers all the benefits of agentic AI we've talked about above. It runs in your IDE or terminal, reads codebase context, recalls specific project instructions via markdown files, can coordinate sub-agents and run multi-step projects independently. It tends to be best for high-level architecture, vision, planning, and implementation. It works fast (some devs say too fast) and sometimes requires more hand-holding.

According to our research, Claude tends to produce the most errors (compared to human-generated PRs) around IDOR / missing tenant check (1.75×), stale comment / wrong doc (1.69×), off-by-one (1.64×), and XSS (1.57×). Auth bypass is at 1.50×.

Gemini CLI: Best for users on a budget

Gemini CLI's biggest benefits include its generous free tier and massive context windows, making it great at ingesting and drawing on large, complex repos. Many devs agree that it's not quite as good at writing clean, error-free code as Codex or Claude Code, but it offers similar features and the budget and memory will be beneficial for some teams.

Greptile: Best AI review tool for agentic coding

As you increase the level of agentic involvement in your development workflow, you need increased code and security review to balance the risks. Greptile is an agentic coding security review tool that solves validation. It helps you take advantage of all the benefits of agentic coding, while mitigating risks and ensuring you can ship code you can stand behind, no matter how complex your codebase. It also works agnostically, no matter what coding agent you use, and understands the context of your entire codebase, so output is truly custom to your code.

Given that agentic models are worse at finding bugs in their own code, you don't want to design a workflow that asks Codex to review Codex code, for example. Instead, Greptile steps in, autonomously, and works with your coding agent to review code with 256% better results.

Greptile also runs security review, does sandbox testing to catch errors that only show up once live, and reviews code from third party APIs with additional context. So if you want Stripe code reviewed, Greptile reviews the change against guidance supplied directly by the partner: their docs, implementation rules, and common failure patterns. With higher context, you get higher quality code, the first time your devs review it.

Agentic coding best practices

• Measure twice, cut once. Planning and preparation matter even more when pulling agents into your SDLC. Make sure that you've thought through the high-level architecture before setting up workflows and agents.
• Give agents the right context. More context is not always the answer, but giving agents the right context means they have what they need to work independently. For example, give agents access to your codebase so they can search and review code on their own. Context engineering can help you reduce errors by making sure the agent has access to what it needs, but nothing extraneous.
• Outsource what's easy, focus on what's hard. Outsource the easy stuff: code generation, boilerplate, stacked PRs, prioritizing code reviews, etc. Focus on the harder, high-level stuff yourself.
• Implement safeguards and manage risks. Don't give your agents access to prod. Keep human reviewers in the loop. Diversify agents to catch more errors and produce cleaner, tighter code. Use sandboxes for testing. Don't expect that agents will act securely. Instead, enact safeguards that ensure they can't do otherwise.
• Set up agentic loops to review and fix code automatically before a human reviews it. Agentic code review tools like Greptile can check PRs against your entire codebase, run security testing, and do sandbox testing to find errors. Use these independently to run agentic loops of feedback and fixes between agents to get more prod-ready code faster.

Agentic AI coding can be a force-multiplier for your team. But agentic coding workflows require more review and security, as you are allowing agents to act on your behalf. When you manage risks effectively, you get higher-quality code you can trust, without spending all your time on code review.

Try Greptile today to see how it can complete the full autonomous loop in your agentic coding process →