Using AI in the Software Development Lifecycle

Most conversation about AI in software development starts and ends with code generation. Models and coding agents are helping engineers generate code faster than ever. But the software development lifecycle (SDLC) is much more than just writing code. SDLC invovles activities like planning, reviewing, deploying, operating, debugging, learning from failures. Engineers work across coding and prodution, and in most cases production work consumes far more time than the writing itself.

Here's a practical look at where AI is making a real difference across each phase of the SDLC, where the biggest gaps remain, and what's coming next.

Design

AI assistants are increasingly useful in the earliest stages of development. They can help draft design documents, generate technical specs from loose requirements, evaluate proposed architectures against known patterns, and estimate scope based on historical data. For teams that struggle to get from idea to structured plan quickly, these tools compress a process that used to take days into hours.

One thing that's underappreciated in planning is the role of production context. The best architectural decisions aren't made in a vacuum. They come from understanding how your systems actually behave under load, what carries hidden dependencies, and where past incidents have clustered. Teams that can bring real production knowledge into the planning phase (whether through AI tooling or better observability practices) consistently make better design choices and avoid repeating costly mistakes.

Writing code

Coding agents and models have gained a lot of adoption in the last few months. Code completion, generation, and inline suggestions are now standard across most engineering teams. Engineers use AI to scaffold new features, translate between languages, generate boilerplate, and accelerate through the kind of repetitive work that used to eat up afternoons.

The tooling here is mature. Most teams have adopted some form of AI-assisted code writing, and the conversation has moved past "should we use this?" to "how do we use this well?" The open questions are about quality control, context management, and how to keep AI-generated code maintainable over time — not whether the tools are useful.

Review

AI is proving valuable for automated code review, test generation, and regression detection. These tools catch issues that manual review might miss, especially in large codebases where no single reviewer can hold the full context. They're good at flagging patterns likerepeated anti-patterns, security vulnerabilities, and performance regressions. These are the issues where engineers may miss when reviewing dozens of PRs a week.

Test generation is a particularly promising area. Writing tests is one of those tasks that engineers know is important but consistently deprioritize under delivery pressure. AI that can generate meaningful test coverage reduces the friction enough that it gets done.

Deploy

CI/CD pipelines are already well-automated, so AI's role here is more about adding intelligence on top of existing infrastructure. Predicting risky deployments based on change patterns, flagging configuration drift between environments, suggesting rollback conditions. These are incremental improvements on a phase that's relatively mature.

The biggest opportunity in deployment isn't AI doing more. It's AI helping teams deploy more confidently by connecting deployment decisions to production outcomes. When you can correlate a specific deploy with a spike in error rates or latency, you start making better shipping decisions.

Operating and Debugging

This is where engineers spend the most time.

Alert triage, incident investigation, root cause analysis, production debugging. They require deep cross-system knowledge: reading logs from one service, correlating with metrics from another, checking recent deploys, understanding how a queue configuration change three weeks ago might be causing timeouts today. This kind of reasoning is hard to automate and even harder to scale across a growing team.

Today, most companies handle this with a patchwork approach. In-house scripts for common runbook steps. Off-the-shelf LLMs for small tasks like summarizing log output or drafting postmortem documents. These are useful, but they automate the edges while leaving the core investigation work entirely on the engineer.

The larger opportunity is fundamentally different: AI that can actually operate running software. Not just summarize a log file, but triage an alert by pulling context from across your code, infrastructure, and telemetry. Not just answer a question about a single metric, but investigate an incident the way a seasoned engineer would like forming hypotheses, testing them across systems, narrowing down root cause.

That's a different category of AI than code completion or test generation. It requires deep understanding of production systems, the ability to reason across domains, and context that persists and compounds over time. It's also the category with the highest leverage because production issues don't wait for business hours, and the tribal knowledge needed to resolve them is usually locked in a few people's heads.

Learn & Improve

The final phase is often the most neglected: learning from what happened. Postmortems get written and forgotten. Operational patterns repeat. Institutional knowledge stays siloed with the engineers who happened to be on-call.

AI can help close this loop like synthesizing patterns across incidents, surfacing recurring failure modes, making tribal knowledge searchable instead of ephemeral. Teams that invest in this phase compound their resilience over time. Every incident becomes training data, not just a fire to put out.

The Bigger Picture

If you map where AI investment is concentrated today, it's heavily weighted toward the "write" phase. Code generation gets the most attention, the most funding, and the most adoption. But for most engineering organizations, writing code is a small fraction of the total work.

The phases that consume the most engineering time (operating, debugging, investigating, learning) are where AI is least mature and most needed. Closing that gap is the next frontier.

What is Resolve AI

Resolve AI is built for the phases of the SDLC that code assistants don't touch like operating, debugging, and learning from production systems. It works across your code, infrastructure, telemetry, and organizational knowledge to investigate incidents, triage alerts, and help engineers understand their production environment deeply.

If you're looking to bring AI into the parts of the SDLC where your team spends the most time, see Resolve AI in action.