Agent-based LLM workflows move beyond single-shot prompting by giving models the ability to reason, plan, and use tools across multiple turns. Instead of returning a final answer immediately, an agent loops through observation, thought, and action steps until it satisfies a goal. This pattern unlocks complex automation, from deep research and code generation to multi-step data pipelines. However, agentic systems also multiply token volume, context length, and API calls, which makes infrastructure choice a first-class design decision.
What Are Agent-Based LLM Workflows?
An agent-based workflow treats the language model as a reasoning engine situated inside a control loop. The loop typically consists of four stages: the model receives a task and available tools, it reasons about what to do, it invokes a tool or generates output, and it observes the result before deciding on the next step. This cycle repeats until the model produces a final answer or hits a termination condition. Because each turn appends new context to the conversation, agentic runs are inherently multi-turn and often long-context workloads.
Core Architectural Patterns
Most production agent systems fall into one of several well-defined patterns. Choosing the right one depends on task complexity, latency requirements, and how much autonomy the model needs.
- ReAct (Reasoning and Acting): The model interleaves reasoning traces with tool calls in a single loop. It is simple to implement and debug, making it the default pattern for many tool-use agents.
- Plan-and-Execute: The model first generates a step-by-step plan, then executes each step sequentially or in parallel. This reduces redundant tool calls but requires a second pass to synthesize results.
- Multi-Agent Orchestration: Multiple models or model instances collaborate, each with a specialized role. A router, coder, and verifier might work together, passing state through a shared message bus.
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