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Agentic Workflows

What it is

Agentic workflows are design patterns where Large Language Models (LLMs) are not just used for single-turn responses, but are part of a multi-step, iterative process where they can reason, use tools, and make decisions to achieve a goal.

What problem it solves

It enables the automation of complex tasks that require more than a single LLM call, such as multi-step research, software development, or sophisticated data analysis, by allowing the model to "think" and act over several turns.

Where it fits in the stack

It is the Orchestration and Reasoning Layer of the AI stack. It sits above the Intelligence Layer (individual models) and integrates with the Tool/Action Layer (APIs and services) to complete end-to-end tasks.

Core concepts

  • Planning: The agent breaks down a complex goal into smaller, manageable steps.
  • Tool Use: The agent can interact with external systems (APIs, databases, web browsers) to gather information or perform actions.
  • Reflection: The agent evaluates its own performance or output and makes adjustments to its plan or behavior.
  • Multi-agent Collaboration: Multiple specialized agents work together, each handling a part of the overall workflow.

Typical use cases

  • Autonomous Coding Assistants: Agents that can write, test, and debug code (e.g., Claude Code, Aider).
  • Complex Research Tasks: Agents that can search the web using Tavily, synthesize information with Claude 4.7, and write reports.
  • Personal Assistants: Agents that can manage calendars, book flights, and handle emails using GPT-5.5 and Llama 4 Maverick.

Strengths

  • Handles Complexity: Can solve problems that are too difficult for a single LLM prompt.
  • Greater Autonomy: Reduces the need for constant human intervention in complex tasks.
  • Improved Performance: Iterative reasoning and reflection can lead to higher-quality results.

Limitations

  • Reliability Issues: Agents can sometimes get stuck in loops or make poor decisions over multiple turns.
  • Cost and Latency: Multi-turn workflows consume more tokens and take longer to complete.
  • Security Risks: Giving agents the ability to use tools requires careful management of permissions and trust boundaries.

When to use it

  • When a task requires multiple steps, tool use, or iterative refinement.
  • When you want to automate a complex process that previously required significant human oversight.

When not to use it

  • For simple, straightforward tasks where a single LLM call is sufficient.
  • When high speed and low cost are the primary requirements.

Sources / References

Contribution Metadata

  • Last reviewed: 2026-06-07
  • Confidence: high