FROM WORKFLOWS TO AGENTS: A PRACTICAL BLUEPRINT FOR LLM TOOL-USE LOOPS
The article clarifies the real difference between LLM-powered workflows and true AI agents and outlines a concrete agent architecture pattern. In [The AI Agent...
The article clarifies the real difference between LLM-powered workflows and true AI agents and outlines a concrete agent architecture pattern.
In The AI Agent Blueprint, Architecture Weekly explains when a workflow with an LLM step is enough, and when you need an agent. The core shift is control. The agent asks what to do next, picks a tool and parameters, runs it, then repeats with feedback.
The piece breaks down the building blocks: a job-like system prompt, a catalog of tools, references to subagents, and guardrails. It uses a Jira ticket triage example to show how an agent chooses actions instead of following a fixed flow.
Helps teams decide when to keep deterministic workflows versus where agentic loops add value for messy, open-ended tasks.
Defines concrete pieces you can standardize: system prompt contract, tool catalog, call–feedback loop, and guardrails.
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Prototype a Jira ticket readiness agent using 2–3 internal tools; compare quality, latency, and cost against a fixed LLM step.
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Stress-test tool parameter validation and guardrails to measure failure modes, retries, and blocked unsafe actions.
Legacy codebase integration strategies...
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Wrap existing internal APIs as tools with strict schemas and scoped auth; add audit logs for every tool call and LLM decision.
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Convert one LLM step into an agentic loop first; track step counts, API call churn, and incident rates before scaling.
Fresh architecture paradigms...
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Design agents as orchestrators over a typed, stateless, idempotent tool catalog to simplify retries and error handling.
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Treat system prompts and guardrails as versioned code artifacts with CI checks that validate tool schemas and policies.