𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗟𝗼𝗼𝗽
If you build with tool-calling models, your most important decision is not the prompt. It is the loop around the model.
An LLM decides to use a tool, but it cannot run that tool. The application or SDK must assemble the context, run the tool, and append the results. This cycle is the agent loop.
The model is only one part of the system. The harness or SDK manages orchestration. This includes prompt assembly, tool execution, retries, and termination.
Key concepts for building reliable agents:
- State management is vital. If you lose tool outputs, the agent forgets what happened.
- Performance depends on prompt growth control. Use stable prefixes and caching to keep costs low.
- Safety requires validation. Use approval gates for actions that change data.
- The harness, not the model, manages the lifecycle.
A practical mental model for the loop:
- Build the input state.
- Call the model.
- Inspect the response.
- If the model requests tools, validate and execute them.
- Append tool results back into the context.
- Call the model again.
- Stop only when the model provides a final answer.
Two systems can use the same model but behave differently. This happens because their harnesses make different decisions about context, tool ordering, and history.
Watch out for these common issues:
- Repetitive behavior: Usually caused by broken state continuity.
- Low quality: Often caused by tool outputs that are too long or noisy.
- High costs: Usually caused by poor prompt ordering that breaks caching.
- Unsafe actions: Occurs when you do not validate tool arguments before running them.
The model chooses actions. The harness controls reality. If you want a better system, do not just tweak the prompt. Build a better loop.
Optional learning community: https://t.me/GyaanSetuAi