Three Emerging Models of Agentic Development
Agentic development tools are no longer a single pattern; they’re crystallising into three distinct models, each suited to different work. First is the IDE-centric model, where AI agents live inside tools like Visual Studio Code, Cursor, Windsurf, or Google’s Antigravity. Here, the IDE is still home base, and agents are powerful collaborators rather than replacements. Second is the chat-first, human–agent collaboration model, centred on platforms such as Slack, Discord, or Telegram. Agents join existing channels, accept tasks, and report back in the same streams humans already use. Third is the orchestrated workflow model, using frameworks like LangGraph or CrewAI to build hybrid pipelines that combine deterministic steps with LLM-driven reasoning. All three models are valid and already coexisting, but they optimise for different needs: deep code work, conversational coordination, and structured multi-step processes respectively.
Why IDEs Still Win the IDE vs AI Coding Debate
Despite the hype around agentic development tools, IDEs remain the productivity backbone because they excel at observability. An IDE isn’t just a code editor; it’s a rich, visual representation of project state: file trees, diff views, git graphs, test outputs, and execution traces. When an AI agent edits code inside this environment, you can immediately see what changed, why it matters, and how it fits into the wider codebase. You can choose to accept the change, refine it, or roll it back with full context. That level of transparency is hard to match in a pure chat interface where an agent disappears for a while and returns with a result. For serious engineering work—where verification, learning, and accountability matter—this observability keeps humans in the loop as informed orchestrators, not passive recipients of opaque AI output.
The Slack-Style Model: Useful, But Not a Full Replacement
The Slack-style model imagines agents as teammates in your communication tools: you assign tasks, they respond with updates and artefacts, all inside familiar channels. This is exactly where offerings like Roo Remote are pointing—bringing agents to where work conversations already happen instead of forcing teams into entirely new environments. For coordination-heavy workflows, this makes sense. Agents can triage requests, summarise discussions, generate documents, or trigger scripted actions without requiring everyone to live in an IDE. However, the model struggles when deep code inspection is required. Once an agent leaves the channel to modify a complex codebase, visibility drops. You see the outcome, not the path taken. That’s acceptable for lightweight automation, but risky for core software changes. As a result, chat-based collaboration shines as a “meeting room” for humans and agents, not as the primary surface for end-to-end development.
Orchestrated Agentic Workflows: Graphs, Pipelines, and Inspection
Orchestrated agentic workflows take a different angle: instead of centering on editors or chat, they centre on graphs. Tools like LangGraph and CrewAI let teams design pipelines where some steps are deterministic (traditional code) and others are handled by LLMs. Each node in the graph represents a task—such as collecting data, generating hypotheses, or transforming outputs—and the entire flow is traceable. Paired with observability tools like Langfuse, you can inspect every input, output, and decision point. This is especially powerful for research workflows, multi-step data processing, or complex evaluations, where reproducibility and auditability matter. In this model, AI-assisted development looks less like a conversation and more like a visual program that happens to be powered by language models. It doesn’t replace the IDE; instead, it complements it by handling higher-level processes that wrap around traditional coding tasks.
Roo Code, Remote Sessions, and the Future of AI-Assisted Development
Roo Code illustrates how AI-assisted development is evolving pragmatically rather than ideologically. As a Visual Studio Code extension, the Roo Code extension embeds an AI assistant directly into the IDE, supporting agentic workflows where developers keep full visibility over changes. Even as its roadmap shifted and then recommitted to the extension, the team doubled down on Roo Remote, exploring a Slack-first agent model instead of trying to replace IDEs outright. In parallel, the fragility of laptop-based development—crashing sessions, lost terminals, ephemeral environments—is pushing interest in more persistent, remote setups where agents and humans can reconnect to long-running workspaces. At StarkMind, for instance, eight AI coding agents span three IDEs and multiple models, with no single vendor as a point of failure. The signal in all this: the future isn’t a singular “AI dev tool,” but a layered ecosystem where IDEs, chat spaces, and orchestration graphs each play to their strengths.