How to build an agentic layer for codebases

TL;DR

The agentic layer lets your agents run the codebase and coordinate tasks across the application, enabling workflows that can ship more with less direct human control. It scales from a simple prompt memory setup to a full orchestrator driven system, culminating in a codebase that can operate with minimal manual intervention.

Transcript

There is one mental framework that sits at the center. An idea so important that if you capture it, it can change the way you engineer forever. The agentic layer. This is the new ring around your codebase where you teach your agents to operate your application on your behalf as well and even better than you and your team ever could. Focusing on bui... Read More

Key Insights

• The agentic layer sits outside the application layer, surrounding it to enable agent control over the codebase.
• Class 1 grade 1 represents the thinnest agentic layer with a prime prompt and memory file.
• Grade 2 adds sub agents and planning documents, enabling more complex workflows and better context handling.
• Grade 3 introduces custom tools, skills, and tool access to enable true automation and orchestration.
• An orchestrator can kick off end-to-end AI developer workflows, enabling automated planning, building, reviewing, and fixing.
• Bundling multiple repositories under the agentic layer allows agents to see and operate across the whole codebase.
• The codebase singularity is the point where agents run the codebase better than the human team, increasing trust and speed of shipping.
• The lesson emphasizes practical steps to move from a linear setup to a parallelized, orchestrated agentic system.
• More videos from this channel:
  • How to Build an Observable Agent Software Factory
  • What Is the Right Way to Use Claude Agent Skills
  • How Claude Code Rewrites Engineering with Agentic Coding
• More from this channel: IndyDevDan

Questions & Answers

Q: How does the agentic layer change the control model for a codebase

The agentic layer creates a distributed control model where agents with prompts, memory, and tools can act on the codebase across its layers. This shifts decision making from a single engineer to a structured set of agents that can plan, execute, and adjust workflows. In practice, this means more consistent automation, faster iteration, and improved reliability as agents can handle routine tasks, coordinate between modules, and maintain context through a memory file and prime prompts.

Q: What are the three concrete classes of the agentic layer

The three concrete classes describe progressive capability. Class one provides a minimal agentic layer with a prime command and memory to drive basic operations. Class two adds planning prompts and sub agents to divide work and improve context handling. Class three introduces custom tools, skills, and tool access, enabling a fully orchestrated system that can run end-to-end workflows and manage complex tasks with minimal human input.

Q: What does class two enable that class one does not

Class two enables more robust workflows by introducing planning documents and sub agents, which allow the system to break tasks down into steps before execution. This yields better planning, reduces ad hoc execution, and improves traceability. It also supports documentation collection through AI docs, facilitating knowledge sharing within the organization.

Q: Why are sub agents important in the agentic layer

Sub agents are important because they specialize in specific tasks, such as fetching docs or writing tests, which lets the main agent focus on higher level coordination. This modularity improves scalability and parallelizes workflows, enabling faster response times and more reliable handling of complex operations across the codebase.

Q: What role does an orchestrator play in this architecture

The orchestrator acts as a central controller that can initiate and manage end-to-end AI developer workflows. It coordinates multiple companion workflows, ensures dependencies are respected, and enables a cohesive execution plan across different parts of the codebase. This orchestration is what enables the codebase to move toward the codebase singularity.

Q: What is meant by codebase singularity

Codebase singularity refers to a future state where agents run the codebase more effectively than the humans who created it. In this state, agents can ship features autonomously, manage deployments, and handle maintenance tasks, reducing direct human input while increasing reliability and speed of delivery.

Q: How does bundling repositories beneath the agentic layer affect operation

Bundling repositories beneath the agentic layer ensures agents can see and operate across multiple applications and services. This holistic visibility enables cross-repo coordination, shared tooling, and unified workflows. It also helps build a more resilient system where changes in one area can be propagated and validated across the entire codebase.

Q: What is required to move from class one to class three

Moving from class one to class three requires incremental investment: begin with a minimal agentic layer and a strong memory structure, add planning prompts and sub agents, then introduce custom tools and skills that connect to external services. The goal is an orchestrated system where tools and agents collaborate to manage end-to-end workflows with minimal human input.

Summary & Key Takeaways

• A new architectural ring around the codebase is proposed, called the agentic layer, designed to let agents operate the application on your behalf.

• The progression from class 1 to class 3 shows increasing capability, from basic prompts to specialized sub agents and custom tools integrated with orchestrator workflows.

• The ultimate goal is to reach a codebase singularity where agents can run and ship software with reduced human input.