Agentic AI: Moving Beyond the Chatbot
What defined AI coding tools in 2023 and 2024, a reactive loop where developers typed a prompt and waited for a response, has given way to something structurally different. The industry now centers on agentic AI development, a paradigm where tools don't just answer questions but proactively plan, decompose, and execute multi-step tasks with minimal human steering.
Consider the practical shift. Tools like Jules, Google's asynchronous coding agent, and GitHub's Copilot Agent no longer wait for instructions one line at a time. They clone repositories, read codebases for context, draft implementation plans, write code across multiple files, run tests, and iterate on failures. All before a developer reviews the output. The workflow resembles delegation to a junior engineer more than interaction with a search bar.
This is not a niche experiment. Gartner forecasts that by 2028, 80% of customer-facing processes will be handled by AI agents. That projection signals a fundamental restructuring of enterprise software architecture and the toolchains that support it. For engineering organizations, the implication is direct: agent-capable infrastructure is becoming a baseline requirement, not a competitive advantage.
Anthropic's Claude Code exemplifies the model. Functioning as an autonomous coding agent within the terminal, it enables asynchronous repository management where developers can offload entire feature implementations, from scaffolding to test coverage, and return to review completed work. The tool operates across branches, handles file creation and modification, and integrates directly into CI workflows. It shifts the developer's role from writer to reviewer.
The transition from reactive to agentic carries real architectural consequences. Systems must now support long-running agent sessions, parallel execution, and robust sandboxing. Teams that treat these autonomous coding agents as glorified autocomplete will miss the structural opportunity entirely.
Infrastructure Wars: MCP and the Fragmentation of VS Code
Beneath the surface of agentic workflows and spec-driven practices lies a quieter but consequential battle over infrastructure. The Model Context Protocol (MCP) has rapidly emerged as the connective tissue between AI agents and the external tools they need to be useful, from Slack channels and databases to CI/CD pipelines and cloud consoles. As organizations move beyond simple code generation toward agents that interact with entire development ecosystems, MCP provides a standardized interface for these connections. The practical result: engineering teams now require dedicated management dashboards to configure, monitor, and secure the growing web of agent-to-tool integrations.
Simultaneously, the IDE market is splintering. Microsoft's VS Code remains the dominant editor, but specialized forks are carving out significant territory. Cursor has built a dedicated following by deeply integrating agentic AI into the editing experience, steadily growing its market share among AI-forward engineering teams. Google's Antigravity represents a different kind of threat: a well-resourced fork optimized for its own Gemini models. These are not minor skin-deep modifications. They are diverging codebases with distinct product visions, each pulling developers into separate ecosystems.
This fragmentation carries real risk. Extension marketplace compatibility is not guaranteed across forks, meaning teams may lose access to critical tooling when switching editors. Proprietary forks also raise hard questions about long-term support. If a startup-backed fork loses funding or pivots, organizations built around it face costly migrations. Security compounds the problem: third-party marketplaces outside Microsoft's ecosystem may lack equivalent vetting processes for extensions, widening the attack surface for supply-chain exploits. For engineering leaders, the IDE choice is no longer a matter of preference. It is an infrastructure decision demanding the same rigor applied to any other platform dependency.
Parallel Execution: The Senior Engineer as Fleet Commander
The infrastructure decisions above set the stage for what may be the most consequential workflow shift of 2026. The most productive engineers are not writing more code. They are running more agents.
A new LLM coding workflow has emerged among senior practitioners: parallel task execution across multiple AI agents simultaneously. Instead of feeding one task at a time to a single agent, engineers now dispatch several at once, each working on a distinct feature, bug fix, or refactoring job. Orchestration tools like Conductor and Verdent AI have surfaced to manage this process, providing dashboards that let a single developer monitor and steer a small fleet of autonomous coding sessions running concurrently.
This approach demands rigorous code isolation. The practical solution is git worktrees, which AI practitioners have quickly adopted as standard infrastructure. Git worktrees allow multiple working directories to exist within a single repository, each checked out to a different branch. When three agents are simultaneously generating code for three separate features, worktrees ensure their changes never collide mid-process. Without this isolation layer, parallel execution devolves into a merge conflict nightmare before any value is produced.
The shift exposes a new constraint. Writing code is no longer the bottleneck. Merging is. When agents produce feature branches faster than a human can review, test, and integrate them, the limiting factor becomes the engineer's own cognitive bandwidth for quality assurance and conflict resolution. The role transforms accordingly: senior engineers increasingly resemble fleet commanders, defining objectives, allocating resources across agents, and making final integration decisions. The skill that matters most is no longer typing speed or syntax recall. It is the ability to maintain a coherent mental model of an entire system while dozens of changes converge toward a single main branch.
The Reality Check: Quality Control and Human Oversight
Speed without verification is just technical debt with extra steps.
Anthropic reports that roughly 90% of code in some environments is now AI-generated. That figure sounds impressive until you consider its corollary: the surface area for subtle, hard-to-detect bugs has expanded dramatically. AI code quality depends entirely on what happens after generation.
Simon Willison, a widely respected voice in the developer community, warns that LLMs remain "over-confident," producing plausible but flawed logic with zero hesitation. The code compiles. The tests pass. The edge case fails silently in production three weeks later.
This makes human-in-the-loop oversight and rigorous automated testing pipelines non-negotiable. Automated test suites, static analysis, and thorough human code review form the only reliable safety net against confidently wrong output.
Organizations also need a metrics reset. Measuring "lines of code produced" in an era of autonomous generation is meaningless. The relevant metric is verified features shipped: working, tested, production-ready functionality. Teams that fail to make this pivot will accumulate technical debt at machine speed, burying themselves under a mountain of code nobody fully understands. The agents write fast. Humans must verify faster.