How AI Development Is Moving from Specialized Agents to Orchestration

Specialized coding agents got AI development off the ground. But as general-purpose models like Claude Code and Gemini CLI have matured, the real leverage is shifting up a layer — to orchestration. This is where engineering teams coordinate many agents at once with parallelism, clean workspaces, and human oversight built in.

Louis Knight-Webb, co-founder of Bloop, has seen this evolution first-hand. In a recent AI Hot Takes conversation with Codegen CEO Jay Hack, he traced the path from enterprise code search to COBOL modernization and now to Vibe Kanban, an orchestration platform for running multiple coding agents in parallel. 

Let’s dive into why the future of agentic development depends less on specialized bots and more on the systems that direct them.

From vertical tools to general-purpose agents

When AI coding first caught on, most solutions were vertical: “coding agents for X,” like code search or a one-off COBOL modernization pipeline. These tools proved that autonomous development was possible and gave teams a safe place to experiment.

Louis described the progression inside Bloop: 

“We started with code search that became enterprise code search… One of our customers loaded in a COBOL codebase… we realized that a lot of organizations wanted to modernize COBOL and spent 18 months working on that problem and building a fully automated end-to-end pipeline coding agent experience.”

But general-purpose models quickly overtook those narrow solutions. Reinforcement learning loops, bigger context windows, and richer APIs meant that a single agent could now handle what previously required bespoke design. As Louis put it, they realized “coding agents were moving in a way that really benefited more general purpose approaches rather than more specialized coding agents.”

Why orchestration emerged as the next layer

Once general agents became capable, the next bottleneck quickly became coordination. Running one agent is straightforward; running dozens efficiently is not. Without the right system, teams waste time watching logs and waiting for sequential runs to finish.

Vibe Kanban was built to solve this. “It’s just basically a way to orchestrate Claude Code, Gemini CLI, AMP and other coding agents at scale,” Louis explained. Instead of queuing tasks one by one, Vibe Kanban manages parallel execution with proper sandboxing and a workflow designed for fast-moving projects.

This shift is bigger than a single product. As agents complete tasks in minutes instead of days, orchestration such as task management, isolation, and reproducibility becomes the new foundation for serious software development.

The orchestration playbook

An effective orchestration layer focuses on engineering fundamentals:

• Parallelism with clean state. Each task runs in its own git worktree, ensuring deterministic builds and eliminating side effects.
• Automated setup and cleanup. Environments are built and torn down predictably, so dependencies don’t leak between runs.
• Integrated boards. When tasks complete rapidly, project management has to fuse code, logs, and live previews into a single view.

This helps teams scale AI development to hundreds of concurrent tasks without sacrificing traceability or quality.

Keeping humans in the loop

Even with orchestration, some decisions can’t be automated. Louis was direct about this: 

“The human element of review is very difficult to replace… the bits around the edges like choosing what work to even get done… I don’t think it is going to go away anytime soon.”

Scoping work, making architectural calls, and deciding when a feature is production-ready remain human responsibilities. Good orchestration respects that reality by surfacing the right context and making review and approval fast and reliable.

Designing the right layer

The key architectural question is what belongs in the orchestration layer versus inside the model itself. Models handle code generation and refactoring. Orchestration governs processes: breaking projects into tasks, preparing environments, managing dependencies, and structuring review workflows.

The line isn’t always obvious, but here are a few guidelines to help:

• Keep process in orchestration. Breaking down projects into tasks, preparing clean environments, enforcing dependency checks, and coordinating reviews all belong at this layer.
• Let the model focus on code. Generating, refactoring, or testing code is where large language models excel; avoid embedding these directly in orchestration logic.
• Use models as subroutines, not supervisors. Treat agents as workers that execute well-defined steps, while orchestration handles scheduling and governance.

By separating responsibilities, teams can scale safely and adjust quickly as model capabilities evolve.

How Codegen helps

Codegen was built for this new layer. It provides:

• Agent orchestration at scale with process isolation, reproducible environments, and parallel execution.
• Integrated developer workflows that combine task tracking, logs, and live previews.
• Enterprise-grade security so teams can run agents on production code with confidence.

By focusing on orchestration instead of one-off agents, Codegen gives engineering teams a durable foundation, even as the underlying models continue to evolve.

The bottom line

The story of AI development is changing from specialized coding agents to orchestration. Vertical tools proved what was possible. General-purpose agents made those tools obsolete. Now the opportunity, and the hard engineering work, is in coordinating agents effectively and keeping humans in control of the process.

For engineering leaders, platform teams, and founders building on Claude Code, Gemini CLI, or the next generation of agents, investing in orchestration is no longer optional. It’s how you scale AI-driven development with the reliability and transparency modern software demands.

Ready to get started? Try Codegen for free or reach out to our team for a demo.