Effective development with AI agents at scale requires a fundamental shift in how we structure context and tool access. The primary failure point for most agents on large codebases is the reliance on RAG (Retrieval-Augmented Generation) architectures that attempt to semantic-match queries against a central database. This approach has been rendered obsolete by file-system-based navigation, where agents use bash commands to probe the directory structure just as a human developer would. By moving away from RAG, you drastically reduce the hallucination rate caused by stale or mismatched code snippets.

The real secret to agent mastery lies in the configuration of the 'harness'. A successful harness is not merely a single prompt but a tiered system of instructions and automation. Developers should decompose their codebase into modular units, creating local claude.md files in every major subdirectory to provide the agent with scoped context rather than stuffing a single file with thousands of lines of instructions. This approach follows the principle of progressive disclosure, ensuring the agent only loads the information relevant to the current task.

Beyond static instructions, developers must leverage hooks—shell scripts that execute automatically during session lifecycle events—to enforce project standards. Configuring a 'stop hook' that forces the agent to reflect on its progress and update documentation is perhaps the most high-leverage move for long-term consistency. Furthermore, the integration of LSP (Language Server Protocol) is non-negotiable for high-quality output, as it enables the agent to navigate code through symbol definition tracking rather than unreliable text matching.

Finally, the architecture should be extended with sub-agents and plugins to facilitate team collaboration. Sub-agents allow for the parallelization of tasks, preventing context window saturation, while plugins turn successful configurations into portable, reusable bundles. Treating your AI agent setup as an evolving piece of infrastructure rather than a static tool is the key to maintaining productivity as your codebase complexity grows.