building-agents

Build a Python tool-using agent by constructing a runtime context, shaping stable prompts, defining a structured tool registry, and implementing parse_response logic for safe LLM-driven reasoning. This provides a runnable agent loop with bounded subagent delegation.

Bounded delegation is a technique for assigning parallel work to subagents while preserving isolation and safety. It allows a Python agent to distribute reasoning tasks without compromising the stability of the main runtime harness.

Manage context and memory for Python LLM agents by capturing repo state and environment in a RuntimeContext, using stable prompt prefixes, and maintaining per-turn dynamic memory sessions to maximize cache hits and minimize token usage.

Safely parse and validate tool calls from LLM responses by applying parsing and validation primitives that guarantee tool inputs are well-formed and safe, using a retry path to handle flaky models and ensure stable execution.

The best way to structure tools for an LLM agent in Python is using a structured Tool model with typed inputs, risk flags, and a simple registry. This keeps agent capabilities explicit, closed, and safe during runtime execution.

No external libraries are required to implement an LLM agent loop. The design relies on native Python primitives for context collection, prompt shaping, result parsing, and bounded subagent delegation without additional dependencies.