Incredibuild Team
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Imagine an AI that doesn’t wait for you to tell it “do this next.” It figures out what needs doing, plans the steps, and executes the task. Meanwhile, it may even call in “assistant AIs” of its own. That’s the promise (and the hype) of agentic AI. And it’s already nudging its way into software development.
Agentic AI refers to AI systems that can act autonomously to achieve goals, without needing step-by-step instructions from humans.
This type of AI breaks down objectives into subtasks, makes decisions, adapts when things go off course.
To put it loosely: generative AI is like a really smart pen (you ask, it writes). Agentic AI is like a proactive project manager (you set the goal, it figures out how to get there).
Because of that “agency,” it can tackle multi-step, longer workflows instead of just handling single prompts.
This is a subtle but important distinction. Many people use the terms interchangeably, but here’s how scholars draw the line:
| Feature | AI Agent | Agentic AI |
| Scope | Narrow, task-oriented | Broad, goal-oriented, multi-step |
| Autonomy | Limited (often guided or reactive) | Higher autonomy, proactive planning |
| Coordination | Single agent or loosely coordinated | Orchestration of multiple agents |
| Memory / Persistence | Shallow, session-limited | Persistent memory, context across tasks |
| Adaptability | Some adaptation, within narrow bounds | Strong adaptation, can reorganize strategy |
| Use cases | Specific automation, tool integration | End-to-end workflows, planning + execution |
AI agents are good at precise, bounded tasks, while agentic AI handles real-world complexity and adaptation. They are the next step toward being more human.
This is where things get exciting (and a little scary). Agentic AI is starting to creep into real dev workflows. Let’s survey some of the key areas.
One of the biggest friction points in teams is getting newcomers up to speed (e.g., understanding the codebase, grasping architectures, figuring out dev environment setup, etc).
An agentic AI could:
That means less time spent chasing down mentors and more time coding (or napping, whichever you prefer).
Coordination overhead can be expensive: tracking tasks, managing dependencies, orchestrating handoffs, and ensuring everyone’s aware of changes.
Agentic AI could help by:
You can imagine an AI tool that watches your backlog and says: “Hey, Alice’s PR is blocking Bob’s work. Should we refactor module M to reduce coupling?” Or automatically scheduling a sync when dependencies shift.
Yes, this is the big one. Generative AI is already helping write snippets; agentic AI goes further:
A recent survey on “AI agentic programming”* covers exactly this: systems that decompose goals, use tool integration, monitor execution, and adapt.
CI/CD Pipeline Optimization
Continuous integration and continuous delivery can be noisy and brittle. Agentic AI could:
Essentially, it becomes a self-healing, self-optimizing DevOps assistant.
Code reviews are tedious. Testing is even more so. Agentic AI can lend a hand:
With an agentic reviewer, you might push code and immediately get a detailed review, more consistent than a tired human reviewer late on Friday afternoon.
We aren’t handing you a doom scroll, but yes, there are serious caveats.
Because these systems make decisions, an incorrect judgment early in a chain might lead to large downstream mistakes.
If the AI “magically” changes the architecture or rewrites modules, understanding why becomes harder.
If an agent has access to internal systems, builds, or deployment tools, what if it’s compromised?
Agentic AI may call internal tools, access sensitive repositories, or data. Guardrails are essential.
You can’t assume full autonomy. You must define decision boundaries, escalation rules, and safety nets.
If devs stop thinking critically (“The AI did it, it must be right”), quality may degrade.
These models need computing resources. They struggle with very large contexts or long memory across tasks.
Who is responsible if the agent introduces a bug, forks a license violation, or leaks data? Academic work on fluid autonomy and authorship already flags these issues.
So yes: exciting, but hazardous terrain.
We’re still in the early phases. But here’s where things seem headed:
In short, over the next few years, agentic AI may shift from “interesting experiment” to “essential teammate.”
Unlikely (at least not in the near future). More realistically, it will augment engineers by taking on mundane, repetitive tasks so human devs can focus on strategy, architecture, creativity, and debugging tricky edge cases.
Only with strong safeguards: permission boundaries, audit trails, human approvals on critical steps, monitoring, and rollback.
AI tends to make logical mistakes and assumptions. Other errors include hallucinated APIs, misinterpretation of ambiguous specs, overfitting to test feedback, or reinforcing bugs across chained actions.
Yes, it tends to demand stronger infrastructure (compute, model hosting, memory, tool integration). But over time, optimizations and model efficiencies may lower the barrier.
Noncritical pipelines: test environments, static analysis, code reviews, documentation scaffolding. Start small and design clear human oversights.
Sources:
*Wang, H., Gong, J., Zhang, H., Xu, J., & Wang, Z. (2025, August 15). AI Agentic Programming: A survey of techniques, challenges, and opportunities. arXiv.org. https://arxiv.org/abs/2508.11126
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Incredibuild empowers your teams to be productive and focus on innovating.
