The software development landscape is undergoing a seismic shift with the advent of agentic coding.
Agentic coding enables AI systems to write, test, and optimize code with minimal human intervention.
So let's take a look at what it is, and how it helps AI professionals...
What is agentic coding?
The technology is particularly impactful in automated testing, legacy code modernization, and rapid prototyping. For example, AI agents can convert outdated codebases into modern programming languages or generate entire micro-services architectures based on high-level design specifications.
How agentic coding works
Agentic coding operates through a structured loop often described as reason and act. Instead of generating a single output, the system continuously evaluates, executes, and refines its actions.
At each step, the agent interprets the task, decides on the next action, and executes it using available tools. This creates a dynamic workflow where the system adapts in real time based on intermediate results.
- Reason and act loop
The agent begins by breaking down a task into smaller steps. It then selects an action, executes it, and evaluates the outcome before moving forward.
This loop continues until the objective is achieved, allowing for iterative progress rather than a single-pass response. Each cycle improves alignment with the intended result.
- Iterative self-correction
Agentic systems refine their outputs through continuous feedback. When an action produces an unexpected or suboptimal result, the agent adjusts its approach and retries.
This iterative self-correction enables higher reliability over time, especially in complex workflows where multiple steps depend on previous outputs.
- Tool use and system interaction
Agentic coding extends beyond text generation by integrating with external tools and environments.
These may include:
• File system access for reading and modifying code
• Terminal commands for running scripts and managing processes
• Dependency management for installing and updating packages
Kshitij Aranke
What are the risks of agentic coding?
Agentic coding introduces powerful capabilities, along with a new set of security and operational considerations. As systems gain autonomy and direct access to development environments, risk management becomes a core requirement.
The following areas represent key risks that teams should address:
• Unvetted dependencies: Agents can install or update packages during execution. Without controls, this introduces exposure to malicious or outdated libraries.
• Prompt injection: External inputs can influence agent behaviour, leading to unintended actions or access patterns within the system.
• Business logic flaws: Autonomous execution can amplify small logic errors, especially when agents operate across multiple steps or systems.
• Compliance and audit gaps: Actions taken by agents may require clear logging and traceability to meet internal and regulatory standards.
• Vulnerability escalation: Agents with broad permissions can interact with multiple systems, increasing the potential impact of a single vulnerability.
Agentic coding vs traditional AI coding assistants
Agentic coding represents a shift from assistive tooling to more autonomous systems. While traditional AI coding assistants support developers with suggestions, agentic systems can take action across multiple steps and environments.
The distinction becomes clearer when viewed side by side:
| Feature | Agentic coding | Traditional AI coding assistants |
|---|---|---|
| Autonomy | Executes tasks independently based on goals | Provides suggestions that require user approval |
| Task scope | Handles multi-step workflows across systems | Focused on single prompts or code snippets |
| Self-correction | Iteratively refines outputs through feedback loops | Limited refinement based on follow-up prompts |
| Tool usage | Interacts with file systems, terminals, and dependencies | Typically restricted to code generation within an editor |
| Human involvement | Operates with defined oversight and control layers | Requires continuous user input and direction |
A shift in workflow design
Traditional AI coding assistants enhance productivity within existing workflows. Agentic coding introduces a different model, where systems participate more actively in execution.
This shift changes how teams structure development environments, define responsibilities, and manage oversight. As autonomy increases, so does the importance of clear boundaries and validation processes.
Andrew Lovell
Best practices for secure agentic coding
Agentic coding introduces new capabilities across development workflows. To ensure consistent performance and control, teams need clear structures for governance, validation, and monitoring.
- Governance and scope control
Effective implementation starts with defining clear boundaries for agent actions. Teams benefit from restricting access to sensitive systems and data, while introducing approval workflows for higher-impact tasks.
- CI/CD integration
Agentic coding performs best when integrated into existing CI/CD pipelines. Routing agent-generated outputs through standard build, test, and deployment processes ensures consistency with engineering standards.
- Dependency scanning
As agents interact with external libraries and packages, dependency management becomes a key consideration. Validating sources before installation, scanning for known vulnerabilities, and tracking changes introduced by agents all contribute to a more secure development environment.
- Audit logging and monitoring
Visibility into agent activity supports both security and operational performance. Logging system interactions and monitoring for unusual behavior enables teams to maintain oversight and respond quickly when needed.
Bringing it together
Agentic coding delivers the most value when treated as part of core infrastructure. Clear controls and strong visibility allow teams to scale these systems while maintaining reliability and trust.
Emerging market leaders in agentic code generation
While established tech giants like GitHub and OpenAI dominate headlines, a new wave of innovative companies is making significant strides in agentic code generation.
• Bolt (StackBlitz): Browser-based development with real-time code generation
• GitLab Duo: Integrated AI across CI/CD workflows
• Sourcegraph Cody: Context-aware coding for large codebases
• Replit Ghostwriter: Real-time coding assistance within collaborative IDEs
Andrew Lovell
The future of agentic coding
As the technology matures, we can expect agentic coding to become even more sophisticated.
Key trends to watch include:
Autonomous DevOps: AI agents will take on more responsibilities in the software development lifecycle, from coding to testing, deployment, and monitoring.
Enterprise adoption: As governance and security concerns are addressed, more enterprises will adopt agentic coding tools to modernize legacy systems and accelerate digital transformation.
Conclusion
Agentic coding redefines how software is developed, tested, and deployed. While established players like GitHub and OpenAI continue to lead, emerging innovators like Bolt (by StackBlitz), GitLab Duo, Sourcegraph, and Replit are pushing the boundaries of what’s possible.
The convergence of AI and software development creates a new era of productivity and efficiency. The future of coding is autonomous, and these emerging leaders are at the forefront of this transformation.
To connect with AI builders breaking down deployment challenges, check out some of AIAI's in-person summits this year:
→ Agentic AI Summit New York
→ Generative AI Summit Silicon Valley
→ The entire 2025 calendar
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