AI agent parameters and why prompting isn't enough
Struggling with AI agents that loop? Learn how tuning hidden parameters like temperature and token limits creates more reliable, cost-effective systems.
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We often treat agents like enchanted prompts, but they’re actually software systems. When they fail, it’s usually because we haven't tuned the 'hidden knobs' or parameters correctly.
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Using Ai agents and setting the right parameters for them
Foire aux questions
Inference parameters are the "hidden knobs" of an LLM engine, such as temperature, top-p, and frequency penalties, that control how a model generates text. While prompts provide the instructions, these parameters determine the statistical behavior of the engine. For example, lowering the temperature can make a model more deterministic and reliable for tasks like data extraction, while adjusting the frequency penalty can prevent an agent from getting stuck in a repetitive loop by penalizing words that have already been used.
A Ghost Action occurs when an AI agent claims to have completed a task—such as booking a flight or sending an email—but never actually executes the underlying tool or API call. This happens because the LLM is designed to be helpful and "hallucinates" a success message to satisfy the user's request. To catch this, developers must move beyond looking at the final text output and instead use execution traces to verify that a "Thought" was actually followed by a technical "Action" span before the "Final Answer" was generated.
Standard agents often use a "greedy" reasoning framework like ReAct, which processes information one step at a time and can lose track of long-term goals. A Plan-and-Solve architecture splits the workload between two specialized agents: a "Planner" that breaks a large objective into a checklist, and an "Executor" that focuses solely on completing one specific step at a time. This reduces the cognitive load on the model, making it less likely to fail when handling intricate, multi-step processes like marketing campaigns or deep research.
Tool Adaptation focuses on improving the "Observations" an agent receives rather than just the "Instructions" it is given. If an agent provides poor answers because its search tool returns irrelevant data, the solution is to refine the retriever or the data source rather than the agent's brain. By ensuring the tools provide high-quality, domain-specific information, even a simpler or smaller LLM can perform at a high level, making the overall system more modular and efficient.
The Sandwich Defense is a security strategy used to protect agents from prompt injection attacks, where users try to override system instructions. Because LLMs are subject to "primacy and recency" effects—meaning they pay the most attention to the beginning and end of a prompt—developers place the most critical safety guardrails at the very end of the prompt, following the user's input. This ensures the agent's final "thought" before generating a response is a reminder to adhere to security protocols regardless of what the user requested.
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