Building software with agentic development?

Agentic development is an emerging approach to building software with AI agents and agentic coding. Instead of simply assisting developers with suggestions or code snippets, agentic systems go beyond traditional AI assistants by acting on behalf of developers. This includes writing code, running tests, debugging issues, and even collaborating across workflows.

As organizations look to pull more value from AI, agentic development represents a shift from passive tools to active collaborators. This evolution is particularly relevant for IT leaders, architects, and engineering teams seeking to close the gap between AI experimentation and real-world production use and business impact.

What does agentic mean?

Agentic means having the ability to act independently toward a goal. In AI, it refers to systems that use AI agents to plan, make decisions, and take actions across multiple steps with limited human intervention. An AI agent within an agentic system can:

• Perceive its environment.
• Make decisions based on goals.
• Take actions to achieve those goals.
• Learn from past outcomes.
• Adapt to scenarios and human input.

In software development, an agentic system supports developers with their daily work. It understands objectives, breaks them down into tasks, executes those tasks using tools, and refines results over time—all within defined guardrails.

Agentic AI development vs. traditional AI assistance

Instead of requiring constant human prompting, agentic systems can operate with a higher degree of independence, making them especially useful in complex environments where tasks span multiple tools and stages.

How does agentic development work?

Agentic development relies on a structured loop of planning, execution, and refinement. While implementations may vary, most systems follow a similar pattern.

Goal definition

The process begins with a high-level goal provided by a human, such as:

• “Build an API endpoint for customer onboarding.”
• “Refactor this module for performance.”
• “Write integration tests for the checkout flow.”

The agent interprets the goal and determines success criteria.

Task planning

The agent decomposes the goal into smaller, actionable steps. For example:

• Analyze the existing codebase
• Identify dependencies
• Write required functions
• Generate test cases

This planning phase distinguishes agentic systems from simple assistants. They form structured workflows instead of responding to isolated prompts.

Tool-driven execution

Agents use other agents via Agent2Agent Protocol, and external systems exposed through Model Context Protocol servers, to interact with tools that enable them to plan and carry out tasks across the development lifecycle. This combined capability is what allows agentic coding to move from suggestion into real action.

Agents use:

• API-based services for integrating with external systems and data sources.
• Workflows and automation tools for executing and orchestrating business processes.
• Knowledge sources and repositories for retrieving structured and unstructured information.
• Communication and collaboration tools for notifications, messaging, and team coordination.
• Business systems and applications for accessing and updating enterprise data and records.
• File storage and document systems for reading, generating, and managing content.
• Custom functions and services for handling specialized business logic, validation, and operational tasks.

The agent can then perform tasks step by step:

• Writes or modifies code
• Runs tests
• Fixes errors
• Updates documentation

Execution is adaptive, with the agent adjusting its approach based on results and outcomes.

Iterative feedback loops

Agents continuously evaluate outcomes:

• Did the code compile successfully?
• Did tests pass?
• Are performance goals met?

If not, the agent revises its approach and tries again, creating a self-improving loop that leverages memory to learn from previous outcomes and improve future performance.

Human review

Despite increasing autonomy, the human brain is still a critical part of agentic coding. Human review is needed for:

• Approving changes
• Providing corrections
• Setting guardrails

Agentic development is most effective when paired with thoughtful human oversight.

Core components of agentic development

Several foundational elements enable agentic systems to function effectively.

Agents

Agents are the primary actors. Different agents may specialize in specific roles, such as coding, testing, or documentation. They combine reasoning capabilities with execution logic, allowing them to:

• Interpret goals
• Plan tasks
• Coordinate actions

Models

At the heart of agents are AI models, typically large language models (LLMs), often delivered through integrated AI platforms that support development, deployment, and scaling. These models provide (but are not limited to):

• Natural language understanding
• Code generation
• Reasoning capabilities

In practice, agentic systems may use a mix of general-purpose and specialized coding LLMs, depending on the task. These can include models optimized for code completion, refactoring, test generation, and repository-level understanding, as well as domain-tuned models designed for specific languages or frameworks.

Tools

Tools extend what agents can do. Examples include:

• Web search: Finds information from the internet.
• Browser automation: Interacts with websites automatically.
• API connectors: Calls external services and systems via APIs.
• File operations: Reads, writes, modifies, or deletes files in local or cloud storage systems.
• Terminal execution: Runs command-line instructions.
• Code execution: Runs generated or existing code.
• Continuous integration/continuous deployment (CI/CD) pipelines: Builds, tests, and deploys code.
• Low-code/no-code platforms

Memory

Memory allows agents to:

• Retain context across tasks
• Learn from previous interactions
• Maintain awareness of the codebase

In agentic systems, memory is often split into:

• Short-term memory that holds immediate context for the current task or conversation (like recent prompts or intermediate results).
• Long-term memory that stores durable knowledge over time (like project history, codebase knowledge, or user preferences), enabling reuse and continuity across sessions.

Guardrails

Guardrails enforce constraints like access control, code quality, security, and auditability.

Agentic development vs. AI coding assistants

AI support in software development has evolved in stages, from simple productivity aids to increasingly autonomous systems. Understanding these differences helps teams set the right expectations for what agentic development enables.

Autocomplete tools

Basic AI tools provide code completion and syntax hints. Autocomplete and syntax tools offer lightweight assistance by predicting the next line of code or flagging errors. They improve efficiency for individual tasks but remain limited to reactive, single-step support.

Copilots

Copilot-style tools provide richer, context-aware assistance but remain dependent on human direction. They can generate functions, suggest refactors, and explain code, but they still rely on developers to define each step, execute changes, and manage workflows.

AI coding agents

Rather than assisting with isolated tasks, AI coding agents within agentic development systems can plan multi-step workflows, interact with tools, run tests, fix issues, and iterate toward a defined goal with less constant human input.

The shift from suggestion to action

The key difference is the ability to go from suggestion to execution, moving from helping developers to actually completing objectives. While assistants help developers work faster, agentic development enables systems to take responsibility for completing objectives, turning intent into working outcomes under human guidance and control.

Benefits of agentic development

These benefits reflect a broader shift toward human and AI collaboration. Clear review checkpoints, approval workflows, and the ability to intervene ensure that agentic systems augment human judgment rather than replace it.

• Speed: Agents accelerate bug fixing, development cycles, and time to market. They handle multi-step tasks end to end, reducing handoffs and wait times. Teams can move from idea to implementation faster, shortening time to market for new features and improvements.
• Productivity: Developers can focus on design and innovation. By offloading routine and operational work, agentic systems free developers to focus on higher-value activities like architecture, problem-solving, and innovation. This leads to better use of human engineering talent.
• Reduced manual work: Routine tasks like testing and refactoring are automated. Repetitive tasks—such as writing boilerplate code, generating tests, refactoring, and documentation—can be automated or partially automated, lowering cognitive load and reducing developer burnout.
• Iteration: Feedback loops drive rapid improvement. Built-in feedback loops enable rapid testing, validation, and refinement. Agents can quickly identify issues, propose fixes, and retry, supporting faster learning and more incremental improvement.
• Scale: Agentic workflows standardize and scale across teams. Agentic workflows make it easier to standardize development practices across teams. As usage expands, organizations can apply consistent patterns, controls, and quality standards without slowing delivery.
• Quality: Agents can apply consistent patterns, refactor code, and generate tests automatically, helping teams maintain cleaner, more reliable codebases over time.
• Security: Agentic workflows can incorporate security checks directly into development and CI/CD pipelines, helping catch vulnerabilities before code reaches production.

Together, these benefits reflect a broader shift toward collaborative development between humans and AI, where agents amplify developer impact while humans retain strategic direction and oversight.

Examples of agentic development

Agentic development can be applied across the app development lifecycle, from early feature design to ongoing maintenance. These examples illustrate how AI coding agents move beyond isolated assistance to take on coordinated, outcome-driven tasks—working alongside developers to reduce manual effort, accelerate delivery, and improve consistency.

• Feature development: Agents generate and integrate code.
• Code refactoring: Agents optimize and modernize codebases.
• Testing: Agents create and run test suites.
• Debugging: Agents identify and fix issues.
• Documentation: Agents produce documentation automatically.

Risks and governance for agentic development

AI agents should be designed to scale across teams and use cases. This includes support for security and compliance requirements, reliability at scale, integration with enterprise systems, and the ability to evolve as organizational needs grow. Human oversight is critical to building in governance controls to help reduce risk.

• Permissions: Agents often require access to codebases, systems, and data. Without well-defined permissions and least-privilege access controls, this autonomy can increase the risk of accidental changes, data exposure, or policy violations.
• Security: Generated code may introduce vulnerabilities if not carefully reviewed. Security practices such as static analysis, dependency scanning, and secure defaults remain essential in agentic workflows.
• Quality control: Agent outputs can vary in quality and consistency and may require validation. Validation steps, like testing, linting, and human review, help ensure that generated code aligns with architectural standards and performance expectations.
• Auditability: Organizations need clear traceability into what agents did, when they did it, and why. Detailed logs and decision traces are critical for compliance, troubleshooting, and continuous improvement.
• Reliability: Agents may misinterpret goals, operate on incomplete context, or fail in edge cases. Safeguards such as bounded execution, retries, and escalation paths help mitigate these risks.
• Oversight: Humans remain accountable for outcomes. Clear review checkpoints, approval workflows, and the ability to intervene ensure that agentic systems augment human judgment rather than replace it.

How to evaluate agentic development tools

Selecting the right agentic development tools is critical for realizing value while maintaining control. Beyond raw capability, teams should evaluate how well tools fit their existing environments, governance models, and long-term scalability needs.

Codebase awareness

Effective tools should understand the structure, dependencies, and conventions of your codebase. This includes maintaining context across files and repositories, recognizing existing patterns, and adapting to established standards rather than introducing fragmentation.

Tool access

Agents must integrate seamlessly with the broader development ecosystem. This includes IDEs, version control systems, CI/CD pipelines, testing frameworks, APIs, and runtime environments. Secure, well-scoped access is essential to enable meaningful execution without overexposure.

Observability

Execution visibility is essential. Teams need clear visibility into what agents are doing and why. Strong observability includes execution logs, decision traces, intermediate outputs, and performance metrics, enabling debugging, trust, and continuous improvement.

Controls

Enterprise-ready tools provide robust governance features such as role-based access control, approval workflows, policy enforcement, and audit logs. These controls help ensure that increased autonomy does not compromise security, compliance, or quality standards.

Enterprise readiness

Solutions must scale securely across organizations. This includes support for security and compliance requirements, reliability at scale, integration with enterprise systems, and the ability to evolve as organizational needs grow.

How to get started with agentic development

Adopting agentic development is a structured evolution toward human and AI collaboration rather than replacement. For teams exploring how to develop AI agents, success comes from best practices. Start deliberately, pair experimentation with clear controls, and learn as you scale.

• Start with pilot use cases: Begin with small, contained scenarios, like test generation, documentation, or contained feature work. Use this approach to define its purpose, confirm its value, verify the model, and build confidence without introducing unnecessary risk.
• Establish guardrails: Define clear policies for security, access, quality standards, and acceptable use. Guardrails help agents act autonomously while remaining aligned with organizational and regulatory requirements.
• Implement human review checkpoints: Maintain human review at key stages, including code changes, deployments, and critical decisions. These checkpoints help maintain trust, accountability, and quality.
• Define success metrics: Track metrics such as development speed, error rates, adoption, and developer satisfaction. Clear measures make it easier to demonstrate impact and guide future investment when adopting a new technology platform.
• Scale gradually: Expand agentic workflows incrementally across teams and use cases. Gradual scaling allows organizations to standardize patterns, refine governance, and incorporate lessons learned over time.

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