Author: Gurubheemacharya Karjol
Most developers treat LLM tool-calling as a simple function execution. That is a mistake.
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Most developers treat LLM tool-calling as a simple function execution. That is a mistake. The real bottleneck in agentic workflows isn’t the model’s intelligence, it is the schema definition. When you provide a tool with an ambiguous or bloated JSON schema, you increase the likelihood of hallucinated arguments and failed executions. The fix is strict,…
Read MoreStop building linear chains and start building event-driven agent loops.
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Stop building linear chains and start building event-driven agent loops. Most developers start by chaining LLM calls sequentially. That works for simple tasks, but it breaks the moment your agent encounters an edge case or a tool error. The workflow stalls, the context gets muddy, and your user experience suffers. Instead, move toward a state-machine…
Read MoreStop building agent loops that rely on a single, massive prompt. They are brittle, expensive, and a nightmare to debug.
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Stop building agent loops that rely on a single, massive prompt. They are brittle, expensive, and a nightmare to debug. The secret to reliable AI agents isn’t a better prompt; it’s a robust tool-calling orchestration layer. When your agent has access to five or more tools, you shouldn’t ask the LLM to choose the right…
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 state management layer. When you treat the agent flow as a linear sequence, a single hallucination or failed tool call breaks the…
Read MoreMost developers treat LLM tool-calling as a linear request-response loop. But if you want to build robust AI agents, you need to stop thinking about sequential chains and start thinking about stateful execution graphs.
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Most developers treat LLM tool-calling as a linear request-response loop. But if you want to build robust AI agents, you need to stop thinking about sequential chains and start thinking about stateful execution graphs. The biggest point of failure in automation workflows is the feedback loop. When an agent fails a tool execution, it often…
Read MoreMost developers treat LLM tool-calling as a linear process
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Most developers treat LLM tool-calling as a linear process, but that is where your agent reliability breaks down. When you allow an agent to call multiple tools in a single turn, the context window gets messy. The model often struggles to map output from Tool A to the input of Tool B, leading to hallucinated…
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