Beyond Copilot: Where Agentic AI Is Actually Useful in Software Development
Agentic tools are creeping into every stage of software development. Time to separate signal from hype.
Everyone has a Copilot story. Whether it's auto-generating boilerplate or hallucinating nonsense, the dev experience has clearly changed — but only incrementally. Meanwhile, vendors are rushing to slap “agent” labels on everything from linters to workflow bots. It’s a mess.
But underneath the hype, something real is happening. A new category of agentic tools — systems that can autonomously plan, act, and adapt — is starting to reshape the software development lifecycle. These aren’t just code-completion toys. They aim to operate across planning, testing, deployment, and even team coordination.
So where’s the real value today? Not in abstract AGI talk, and not in overbuilt RAG chatbots that pretend to be product managers. But in targeted use cases where agentic tooling helps developers move faster, catch issues earlier, or automate grunt work that no one wants to do.
Let’s break it down, stage by stage.
Defining Agentic AI in the SDLC
First, some boundaries. When we say "agentic," we’re talking about systems with some degree of autonomy: they can interpret a goal, take multiple actions to pursue it, and adapt based on the outcome. Not just autocomplete. Not just a clever prompt.
Agentic AI in software dev often falls into three categories: enhanced copilots (LLM-driven pair programmers), orchestrators (tools that chain together workflows based on context), and autonomous agents (systems that set sub-goals and take actions without step-by-step instructions).
In practice, the best current tools are hybrids. Pure agents still hit limits around memory, state, and decision quality. But used surgically, they unlock serious leverage.
Planning & Requirements Gathering
This phase is still one of the squishiest in the SDLC, which makes it both promising and perilous for AI. Tools like Speculative, Iterate.ai, and internal LLM agents can help turn rough ideas into epics, generate acceptance criteria, and map dependencies. Some teams are experimenting with AI-generated Jira tickets from meeting transcripts or product briefs.
It sounds magical… until you realize the cost of hallucination in this phase is extremely high. One wrong assumption can ripple into weeks of wasted work. Worse, poor planning undermines every downstream benefit of agentic tooling. If you're using agents to move fast, you better be pointed in the right direction.
A common example comes in the form of product requirement docs in RFPs or client spreadsheets that were AI-generated. On the surface, they seem structured and complete. But scratch even slightly and the gaps show: features that don’t apply, critical brand nuances that are missed, priorities that are misaligned. Without careful pruning, these docs send your agentic tools speeding away from the actual goal. Like a racecar with the GPS set wrong— fast, confident, and completely off track.
The best use today is rapid draft generation followed by human pruning. Think intern speed, with senior oversight. And above all, lock in planning clarity before letting agents accelerate execution.
Coding & Implementation
Autocomplete still rules here, but some agentic patterns are creeping in. Tools like Sweep can take a GitHub issue and autonomously generate a pull request (PR), complete with multi-file changes, test updates, and documentation. CodeRabbit can be used to review a PR, and even generate changes. These tools aren’t perfect—at least, not yet—but they hint at what's coming.
Also emerging are more capable autonomous dev agents like Devin or Claude Code, which can tackle entire implementation tasks from end to end: reading a spec, writing code, testing it, and packaging it up for review. They can still be somewhat unreliable on complex systems, but they represent a significant leap in ambition and capability.
The sweet spot: generating scaffolding, integrating third-party SDKs, or executing repetitive refactors. High-confidence, low-context tasks. What doesn't work yet: anything requiring sustained memory, creative problem solving, or deep product understanding. Code is still communication. Agents are still not great communicators.
Documentation
This is one of the most underrated but high-leverage areas where agentic tools are starting to shine. Documentation is often a neglected part of the SDLC: tedious to write, easy to deprioritize, and hard to keep updated. Agentic tools can help flip that script.
Devin, for instance, can now generate full project wikis, synthesize documentation from codebases, and even update READMEs and inline comments based on recent changes. Other tools are emerging that draft API docs, architectural diagrams, or onboarding materials.
The upside is huge, with better internal knowledge sharing, faster onboarding, and clearer communication across teams. But there are risks, including inaccuracies and version control challenges, both exacerbated by the temptation to publish without review. Just because the bot wrote it, doesn't mean it's right. But with light oversight, the lift-to-value ratio is hard to beat.
Testing & QA
This is where agentic tooling is punching above its weight. Tools like Spur can generate test cases from specs, track code changes to suggest new tests, and even run and debug tests autonomously.
AI can help surface edge cases faster than humans would, especially in large or legacy codebases. It can also simulate user flows that might otherwise go untested. Still, flaky tests, shallow assertions, and false positives are common failure modes. Like all testing tools, AI is only as good as the constraints you give it.
Deployment & Monitoring
If testing and QA are where agentic tooling already punches above its weight, the CI/CD phase is where you should expect to see big improvements in the future. It’s ripe for automation, but agentic AI is only just starting to find footholds here with some devops platforms piloting agents that watch metrics and logs, recommend rollbacks, or even automate release notes and stakeholder updates. It’s not where you’ll find the greatest lift today, but in the future it could be.
There’s also growing interest in agents that coordinate multi-step deployment processes, especially in poly-repo or microservices environments. The catch: most teams still rely on tightly-coupled scripts and fragile pipelines. Injecting an AI layer requires robust observability and clear decision boundaries— two things many orgs lack.
Potential Pitfalls: What To Watch For
The real limitation isn’t what agentic tools can do — it’s whether our workflows allow them to do it well. These systems thrive on structure: clear specs, up-to-date documentation, consistent testing practices. But many teams have deprioritized those habits in favor of speed.
To unlock more value, we need to reinvest in the fundamentals: writing clean unit tests, defining interfaces tightly, and documenting decisions. The irony is that AI works best when we slow down just enough to give it what it needs to go fast.
There’s also the issue of trust. Developers aren’t eager to let a black box refactor production code or rewrite tests without oversight. That’s not a tooling problem — it’s a culture and clarity challenge. And it's solvable.
In short: the tools are further along than we are. The next leap in leverage won’t come from better models. It’ll come from teams getting better at setting the stage.
Final Take: What’s Worth Betting On
For now, the most promising agentic tooling isn’t trying to be your replacement. It’s acting like a tireless, mediocre teammate: fast, unoriginal, occasionally wrong — but useful if you know how to direct it.
Teams that benefit the most are those that pair AI with clear processes, good tooling hygiene, and a culture of review. The next wave of productivity won’t come from autonomous coders. It’ll come from agents that help engineers spend more time thinking, less time wrangling.
Use them where they save time. Watch them where they add risk. And remember: even the best agents still need supervision.
Leigh Bryant
Editorial Director, Composable.com
Leigh Bryant is a seasoned content and brand strategist with over a decade of experience in digital storytelling. Starting in retail before shifting to the technology space, she has spent the past ten years crafting compelling narratives as a writer, editor, and strategist.