Agentic Coding

What is agentic coding?

Agentic coding is a development approach where autonomous AI agents take on active roles in writing, testing, and modifying software. Unlike traditional AI coding assistants that focus on single suggestions, agentic coding agents can plan, execute, and iterate on entire coding tasks with minimal human intervention.

An agentic coding assistant operates by breaking down complex requests into steps, generating code, testing the output, and refining results until the goal is met. These agents are not limited to one-off edits; they handle loops of reasoning and execution, learning from prior runs to improve accuracy.

The rise of AI coding agents promises faster delivery of features, automated bug fixing, and reduced developer workload. However, it also changes how organizations must think about governance, as autonomous decision-making introduces new layers of risk. To use an agentic coding tool responsibly, businesses need policies, guardrails, and visibility into how these agents work inside their environments.

Related Content: What is agentic AI?

How coding agents work: autonomy, context, and control

Agentic coding introduces a shift in how software is created. Instead of relying on human developers to guide every line of code, autonomous AI agents handle tasks end-to-end. 

To understand how these systems operate, it helps to break down their functionality into three interconnected capabilities: autonomy, context, and control.

Autonomy: independent execution of coding tasks

At the core of agentic coding is autonomy. Unlike traditional assistants that wait for step-by-step human input, coding agents can independently decide how to approach a task. For example, a developer might request an API integration. The agent generates initial code, runs tests, interprets failures, and then retries with adjusted logic, all without manual guidance.

This independence makes coding agents powerful accelerators. They reduce repetitive work and allow developers to focus on higher-level design. However, autonomy also means the agent may introduce design decisions that conflict with architectural standards or organizational policies. Without oversight, autonomous execution can quickly drift into risky or noncompliant territory.

Context: reasoning beyond single prompts

What distinguishes an agentic coding assistant from a basic AI tool is its ability to reason across context. These agents don’t just generate code in isolation. Instead, they analyze surrounding files, dependencies, and system requirements before making decisions. For example, if the agent modifies a database schema, it understands the impact on queries, APIs, and downstream services.

This contextual awareness enables coding agents to perform multi-step planning. They can chain tasks together, write unit tests, update documentation, refactor related components, and validate runtime behavior. Context reduces errors that would otherwise arise from fragmented, single-prompt coding. But it also means the agent has access to broader parts of the codebase, raising questions of data exposure, dependency sprawl, and ungoverned architectural changes.

Control: governance and human oversight

The third pillar of agentic coding is control. While autonomy and context drive productivity, organizations must establish clear guardrails to ensure safety and compliance. Control mechanisms allow teams to define what the agent can and cannot do. This might include restricting access to production repositories, enforcing code review gates, or requiring approval before dependencies are added.

An agentic coding tool without control creates significant risk: it may introduce vulnerabilities, adopt unvetted libraries, or bypass established secure coding practices. With proper governance, however, these same tools become reliable copilots that accelerate delivery while staying aligned with organizational standards.

How they connect

Autonomy, context, and control work together as a continuous cycle where each reinforces the others. Autonomy drives independent action, context makes those actions informed, and control ensures they remain safe and aligned with business needs.

Risks associated with agentic coding

While agentic coding assistants offer productivity gains, they also expand the attack surface. Without proper governance, AI coding agents can create risks that undermine security, compliance, and business resilience.

• Introduction of vulnerabilities: Autonomous agents may generate insecure logic, weak cryptographic functions, or flawed access controls. These mistakes can slip past developers if the agent’s output is trusted without thorough review.
• Unvetted dependencies: An agentic coding tool might install new libraries or frameworks without checking for licensing issues, known vulnerabilities, or alignment with organizational standards. This mirrors risks seen in broader supply chain attacks.
  
• Business logic flaws: Agents with contextual autonomy can unintentionally alter workflows in ways that break compliance rules or disrupt business-critical functions. For example, modifying transaction limits or altering authentication flows.
• Compliance and audit gaps: If coding agents make changes outside established approval processes, organizations lose traceability. This makes it difficult to provide audit evidence or demonstrate compliance with frameworks like SOC 2 or PCI DSS.
• Escalation of errors: Autonomous loops mean a small mistake can snowball. An agent that misinterprets a failing test may repeatedly patch code incorrectly, compounding issues until the system becomes unstable.
• Data exposure risks: Access to larger portions of the codebase increases the chance that sensitive information, such as API keys or personal data, could be surfaced or mishandled.

These risks highlight why organizations should treat agentic workflows as powerful but potentially dangerous. Controls, monitoring, and structured oversight must be built in from the start.

Best practices for secure agentic coding workflows

Enterprises adopting agentic coding must approach it with the same rigor they apply to other high-impact technologies. The following best practices help ensure AI coding agents accelerate development without compromising security or compliance.

Governance and scope control

Enterprises need strong governance frameworks to ensure agentic coding assistants don’t operate beyond their intended purpose. This starts with clear scoping and guardrails.

• Define scope and guardrails: Restrict agents to non-production environments and limit privileges to prevent uncontrolled code changes.
• Apply strict dependency governance: Require all agent-suggested packages and libraries to pass SBOM and supply chain reviews before approval.
• Enforce compliance evidence collection: Ensure every agent action is logged, preserving audit trails for frameworks like SOC 2, PCI DSS, or HIPAA.

Oversight and integration

Oversight mechanisms reduce the chance that AI coding agents bypass human judgment or organizational processes. By embedding these checks into workflows, enterprises maintain alignment with secure development practices.

• Integrate oversight into pipelines: Route all agent-generated code through automated security checks and mandatory human review before merging.
• Leverage enterprise visibility platforms: Connect agent activity to architecture mapping and governance solutions to prioritize risks effectively.
• Monitor for new classes of vulnerabilities: Use tools like agentic AI vulnerability assessments to detect risks unique to autonomous workflows, including toxic API combinations or hidden design flaws.

Testing and assurance

Even with governance and oversight in place, enterprises must validate that guardrails actually work under pressure. Testing ensures security programs adapt to adversarial threats and evolving attack techniques.

• Run controlled red team exercises: Simulate real-world attack scenarios to probe whether guardrails can be bypassed.
• Perform layered security testing: Combine static analysis, dynamic testing, and adversarial prompts to evaluate how coding agents behave under stress.
• Continuously refine controls: Update policies and testing approaches based on findings, ensuring defenses evolve with the pace of development.

By categorizing best practices into governance, oversight, and testing, enterprises can adopt agentic coding tools responsibly. This structure aligns autonomy with security, making it possible to capture innovation without sacrificing compliance or control.

Frequently asked questions

What distinguishes agentic coding from using AI coding assistants?

AI coding assistants provide suggestions but depend on developer guidance. Agentic coding involves autonomous agents that plan, execute, and iterate on tasks independently, making them more powerful but also riskier without proper governance.

How do coding agents impact code review and merge-request workflows?

Coding agents accelerate delivery but can overwhelm pipelines if unchecked. Enterprises mitigate this by enforcing mandatory human reviews, automated testing, and governance rules that ensure agent-generated changes meet security and compliance requirements.

What are typical vulnerabilities introduced by agentic code agents?

Common vulnerabilities include insecure logic, unvetted dependencies, and architectural misalignments. Because agents act autonomously, even small errors can scale rapidly, creating cascading security or compliance risks if oversight and controls are not in place.

How can organizations enforce secure coding standards for agentic coding?

Organizations embed secure coding policies into CI/CD pipelines, enforce dependency scanning, and require audit logging of agent actions. Combining these controls with training ensures coding agents operate within enterprise standards.

How should the use of coding agents be monitored in production systems?

Production monitoring should track agent activity through logs, anomaly detection, and continuous security scans. Linking results to enterprise visibility platforms ensures teams can trace every action back to the agent and validate compliance.