Designing Robust Agentic Loops

The core of agentic engineering is not just writing loops, but designing them to handle the realities of LLM behavior. A common anti-pattern is treating an LLM as an autonomous executor that can run tools directly. In reality, the LLM is a probabilistic predictor that only outputs parameters for your code to execute.

To build reliable systems, you must branch your logic based on the stop_reason provided by the model. This is critical for two reasons:

  1. Tool Execution: You must detect when the model has stopped specifically to request a tool, execute that tool in your own code, and feed the result back into the loop.
  2. Token Limits: You must monitor for partial responses caused by token exhaustion. If the model stops due to a limit, you must handle the incomplete output rather than assuming it is a finished task.

Context Discipline and Specialization

Avoid the "carpenter with every tool" anti-pattern, where a single agent is overloaded with every available tool. This leads to confusion and poor performance. Instead, follow the principle of specialization: create sub-agents that perform one specific task with only the one or two tools they require.

Furthermore, maintain strict context hygiene to save costs and improve accuracy:

  • Isolation: Fork sub-task outputs into separate threads. Only return the final summary to the main context to prevent "context pollution."
  • Avoid Groupthink: When using a critic agent, provide only the claim and the evidence. Withholding the original reasoning prevents the agents from converging on a single, potentially flawed idea through a process similar to human groupthink.
  • Compaction: Implement token count checks. Once a threshold (e.g., 150,000 tokens) is reached, trigger compaction algorithms to condense the context window.

Production Efficiency

For CI/CD pipelines, ensure agents are configured for non-interactive modes to prevent the system from pausing for human permission. Finally, leverage batch processing for non-urgent tasks. By running workloads in batch mode, you can achieve a 50% reduction in token costs, provided you can accommodate a 24-hour turnaround time.