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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.

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., Devin, Aider).
  • Complex Research Tasks: Agents that can search the web, synthesize information, and write a report.
  • Personal Assistants: Agents that can manage calendars, book flights, and handle emails.

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: 2026-05-07
  • Confidence: high