Author: Gurubheemacharya Karjol
Most developers think tool-calling is just about letting an LLM choose a function. They are missing the bigger picture: the importance of schemas as the API contract for your agent.
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Most developers think tool-calling is just about letting an LLM choose a function. They are missing the bigger picture: the importance of schemas as the API contract for your agent. If your tool definitions are messy, your model’s reasoning will be messy. When defining tools for a framework like LangChain or AutoGen, treat your docstrings…
Read MoreThe biggest mistake I see in building AI agents isn’t the model—it’s the way we handle tool-calling loops.
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The biggest mistake I see in building AI agents isn’t the model—it’s the way we handle tool-calling loops. Most devs implement a naive while-loop that just feeds tool outputs back to the LLM. The problem? Context window bloat and “hallucination drift” where the agent gets lost in its own previous function calls. To fix this,…
Read MoreStop building agents that loop endlessly on the same broken task.
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Stop building agents that loop endlessly on the same broken task. Most agents fail because they lack a robust error-handling strategy for tool-calling. When an LLM receives a 400 error or a malformed JSON response from an API, it often hallucinates a fix or enters a repetitive failure cycle. Instead of hoping the model recovers,…
Read MoreStop treating your LLM agent like a chatbot and start treating it like a state machine.
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Stop treating your LLM agent like a chatbot and start treating it like a state machine. The biggest mistake developers make when building autonomous workflows is relying on prompt chaining without a robust execution loop. When you expect a single prompt to handle retrieval, reasoning, and tool-calling, you inevitably hit the context limit or hallucinations.…
Read MoreStop building monolithic AI agents that rely on a single, massive prompt chain. You are likely hitting a bottleneck in latency and reliability.
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Stop building monolithic AI agents that rely on a single, massive prompt chain. You are likely hitting a bottleneck in latency and reliability. The most robust agentic workflows today use a Router-Coordinator architecture. Instead of asking one LLM to perform every step, use a lightweight, fast model to act as a router that classifies the…
Read MoreStop treating your LLM agent like a chatbot and start treating it like a state machine.
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Stop treating your LLM agent like a chatbot and start treating it like a state machine. Most developers fail when building agents because they rely on single-turn prompt chains that drift. When a task requires multiple steps, the probability of cumulative error rises exponentially. The fix is moving to a formal ReAct or LangGraph pattern…
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