Writing a correct function is not the same as understanding the application it changes. An AI coding tool may produce convincing work in one file while missing consequences elsewhere in a large codebase. Feeding it more source material sounds like a straightforward answer, but a model’s context window limits how much information it can use at once. The challenge is giving the tool the right system knowledge without overwhelming the space available for the task.
Blitzy Field CTO Neeraj Deshmukh joins Mike Vizard to discuss that distinction and the expectations developers bring to AI-assisted work. He argues that vague instructions leave models to resolve ambiguities on their own, allowing small deviations to accumulate. Context is therefore more than a larger prompt. It needs to connect the requested change with the relevant modules, dependencies and intended behavior. Deshmukh cautions that sharing a limited context window across multiple agents can compound the problem rather than remove it.
He describes Blitzy’s approach as building a knowledge layer from the existing codebase, then supplying coordinated agents with the portions each task requires. Before implementation, the agents produce an action plan for human review. That gives the developer an opportunity to correct assumptions and identify missing requirements before code generation begins. Planning agents then determine which tasks can run in parallel and which depend on earlier work. Deshmukh says validation runs alongside generation, using success criteria supplied by the person directing the work.
That division of responsibility leaves an important process question for development teams. Generating code faster can expose weaknesses in review and QA that a slower workflow previously concealed. A growing queue of changes is not a delivery improvement if reviewers and testers cannot keep up. Deshmukh argues that adopting AI requires changes to the software development lifecycle, not simply another coding tool. Teams need to decide how work will be specified, coordinated and validated before treating higher code output as evidence of better results.
Mike Vizard is a veteran IT journalist with more than 25 years of experience covering the technology industry, having previously served as Editor-in-Chief of both CRN and InfoWorld and as editorial director for Ziff-Davis Enterprise, where he oversaw titles including eWEEK, CIO Insight and Baseline. Over his career he has also edited or contributed to a wide range of enterprise technology publications, including IT Business Edge, Channel Insider, ComputerWorld, TMCNet and Digital Review, and he later led editorial for CTOEdge.com. His reporting and analysis span software development, cloud computing, cybersecurity, IT channel strategy and, more recently, artificial intelligence and DevOps practices. A recognized voice in enterprise IT journalism, Vizard is known for tracking emerging technology trends as they move from early adoption into mainstream enterprise use. He now serves as Chief Content Officer for Techstrong Group, where he oversees editorial strategy across the full network — DevOps.com, Security Boulevard, Cloud Native Now, Digital CxO, Techstrong.ai, TechStrong.IT, Techstrong Semi and PlatformEngineering.com — in addition to writing and hosting content for Techstrong TV and the Techstrong Gang podcast.