What Makes Development “Agentic”?

In our previous posts, we explored the path from traditional to AI-assisted to agentic development. Now let’s unpack what “agentic” actually means in practice, and what truly separates it from simply using AI tools.

Agentic development represents a fundamental evolution in how software gets built: from AI that assists developers, to AI that acts alongside them.

Below are the five key characteristics that define this new paradigm.

1. Autonomy: Beyond Suggestion to Execution

AI-assisted tools suggest; agentic systems execute.

When you use traditional GitHub Copilot, you’re operating in a world of constant micro-decisions. You review every suggestion, hit “accept” dozens of times to complete a single function, and remain firmly in control of each line of code. It’s helpful, certainly faster than typing everything manually, but you’re still the one driving every decision.

With agentic capabilities, the dynamic transforms entirely. You describe what you need at a higher level – “Upgrade this module from .NET 6 to .NET 9” – and the agent takes responsibility for the entire task. It analyzes dependencies, updates affected files and configuration files, adjusts your CI/CD pipelines, and delivers a complete, tested solution.

The agent doesn’t wait for you to approve every micro-decision. Instead, it operates within guardrails you’ve established – your coding standards, security policies, and architectural principles – completing complex multi-step work while you focus on higher-value tasks. You review the outcome, not every keystroke.

This autonomy extends beyond simple code generation. An agent might spend twenty minutes refactoring fifty files, running tests after each change, rolling back when something breaks, and trying alternative approaches until it works. You assign the task Monday morning and review a working pull request Monday afternoon.

The productivity implications are staggering. A developer who once spent three days manually upgrading framework versions now spends three hours reviewing an agent’s work and handling edge cases the agent couldn’t fully resolve. The nature of work shifts from execution to orchestration, from typing to directing.

2. Context Awareness: Understanding the Whole System

Autonomy means agents can act.  Context awareness ensures they act correctly.

Early AI coding tools operated at the file level, suggesting completions based on the code immediately visible in your editor. They had no understanding of your broader architecture, your team’s conventions, or the intricate dependencies that make your system unique.

Agentic systems understand your entire codebase — not just syntactically, but semantically. They grasp your architectural patterns (monolith vs. microservices), naming conventions, and internal frameworks.

Most importantly, they understand your organization’s specific frameworks and libraries, the internal utilities your team has built over years, the custom authentication middleware, the specialized logging framework that routes different message types to different destinations. This institutional knowledge, traditionally locked in senior developers’ heads and scattered across documentation, becomes available to AI.

They also learn from your history. When your team migrates from inheritance to composition or updates dependency-injection patterns, agents adapt automatically. They code like seasoned teammates who’ve read every commit and understand your system’s evolution.

Context-aware agents write code that fits, not just code that works.

3. Multi-Step Reasoning: From Requirements to Implementation

Perhaps the most powerful characteristic of agentic systems is their ability to break down complex requirements into an actionable plan and then execute it autonomously.

In traditional development, a product manager might write: “Add a feature that allows customers to schedule recurring payments.” A traditional developer then translates this into technical architecture: database schema changes to store recurrence rules, new API endpoints for CRUD operations on scheduled payments, background job infrastructure to process payments on schedule, frontend interface for customers to set up schedules, validation logic to prevent invalid configurations, error handling for failed payments, notification systems to alert customers, test coverage for all scenarios, and documentation for the new feature.

An agentic system does the same, but doesn’t stop at the plan. It executes: creating tables, updating code, refactoring when necessary, and resolving blockers along the way. When automated tests fail, it investigates, fixes, retries, and only escalates when human insight is truly needed.

Today’s agents already handle dozens of steps across multiple files. Within months, they’ll routinely complete tasks that once took skilled developers days.

Agentic reasoning transforms requirements into running software, with no hand-offs required.

4. Tool Use: Agents that Act on Your Behalf

Agentic systems don’t just write code – they use the same tools developers use, acting on your behalf throughout the development lifecycle.

They run test suites, analyze failures, and adjust their code. They access API documentation to understand integration requirements, reading through specs to ensure they’re implementing protocols correctly, and even execute code to validate assumptions.

They interact with your CI/CD pipelines, triggering builds, monitoring results, and responding to failures. When a security scan flags a vulnerability in generated code, the agent addresses it. When performance tests reveal a bottleneck, the agent optimizes.

This tool use extends beyond development into operations. Advanced agentic systems can deploy code to staging environments, monitor logs for errors, query metrics systems to verify performance, and even roll back deployments if issues arise.

An agent that writes code is useful.  An agent that ships working software is transformational.

5. Collaboration: Multiple Agents Working in Concert

The real magic begins when agents start collaborating.

The most advanced implementations of agentic development feature multiple specialized agents collaborating on different aspects of development – not just working in parallel, but actively coordinating with each other.

• One agent focuses on feature implementation, generating the core functionality you’ve requested.
• Another specializes in security review, analyzing the implementation agent’s work for vulnerabilities, insecure patterns, and compliance violations.
• A third handles performance optimization, profiling code and suggesting improvements.
• A fourth manages compliance checks specific to your industry, HIPAA requirements for healthcare, SOX controls for financial services, GDPR provisions for EU data.

This collaboration can happen asynchronously or in real-time, depending on the task. For routine features, agents might work through the entire cycle autonomously. For complex or sensitive changes, they might involve human review at key decision points.

It’s like having a 24/7 development crew that ships at machine speed. A system of specialized teammates learning from one another’s outputs.

The productivity multiplier isn’t additive, it’s multiplicative. An organization that masters multi-agent collaboration doesn’t just move faster; it operates in a fundamentally different competitive reality.

Real Examples Happening Today

This isn’t science fiction or vaporware. Agentic development capabilities are live in production environments right now:

GitHub Copilot in agent mode allows you to assign entire issues to AI. You create a task in your backlog—”Implement password reset functionality with email verification” – assign it to Copilot and return hours later to find a complete pull request with implementation, tests, and documentation.

AI-powered security review agents analyze pull requests not just for obvious vulnerabilities but for subtle security implications. They catch issues like potential SQL injection vectors, insecure data handling, missing input validation, authentication bypass possibilities, and compliance violations with standards like GDPR or HIPAA.

GitHub Copilot App Modernization helps you assess applications, apply code transformations, patch builds, and containerize services in days instead of months. This new capability of Copilot helps teams modernize existing Java and .NET projects faster.

Automated pull request generation from natural language issue descriptions is becoming routine. Teams are creating detailed issues in their tracking systems and watching as agents transform those requirements into working code, complete with tests and documentation, ready for human review.

The technology isn’t perfect – agents make mistakes, miss edge cases, occasionally generate solutions that require significant human refinement, and sometimes pursue approaches that technically work but aren’t optimal for your specific context. But they’re improving rapidly, with each new model release bringing measurably better reasoning, broader context windows, and more sophisticated tool use.

Even now, these capabilities are changing the economics of software development. Teams mastering agentic workflows report 3–5× productivity gains — and the gap is widening every month.

The Implication for Your Organization

Agentic development isn’t five years away. It’s here, and it’s compounding.

The teams mastering agentic workflows today are establishing productivity gaps that will be nearly impossible for competitors to close. They’re not just moving faster; they’re learning faster, building institutional knowledge about what works, developing playbooks for human-AI collaboration, and training developers in skills that will define the next decade of software engineering.

Every month you delay represents not just lost productivity but lost learning. The question isn’t whether your organization will eventually adopt agentic development—market forces will make that decision for you. The question is whether you’ll lead the transformation or scramble to catch up.

The future is agentic. Your time to lead is now.