## Use Cases

**Workflow Use Cases:** Workflows are ideal for **structured, repeatable processes**. These are some example scenarios and why a workflow fits:

* *Content generation pipelines:* If you need to generate text in stages – for instance, first have the LLM produce an outline, then validate the outline against some criteria, then flesh it out into a full article – a workflow is a natural fit. Each stage is a well-defined subtask. Anthropic specifically gives the example of writing marketing copy and then translating it, or outlining a document and then writing it. In such cases, the sequence of steps (write → check → rewrite) is known upfront and can be encoded as a prompt chain.
* *Data or query routing:* Suppose an AI system handles customer queries and some queries should trigger a database lookup, others should trigger a predefined answer, etc. A workflow with a routing step can classify the query and then invoke the appropriate subsequent step. Another example is using different LLMs or prompts depending on input complexity (as noted by Anthropic: easy questions go to a smaller, faster model; hard ones to a larger model for better accuracy).
* *Parallel analyses:* Workflows can handle tasks that benefit from parallel processing. For instance, analyzing different parts of a document simultaneously with multiple LLM calls and then aggregating the results is a workflow pattern (referred to as *sectioning* in the Anthropic article). Likewise, having multiple LLM instances attempt the same task and then “voting” on the best result (to increase reliability) is another workflow approach. These are useful when speed or double-checking is important and the task can be split.
* *Deterministic tool usage:* If using tools (APIs, databases, calculators) in a predictable manner is required, workflows allow the developer to precisely control when and how each tool is called. For example, a workflow might always extract certain fields from user input and then call an API with those fields. The LLM might only be used to transform or interpret results, but not to decide *which* API to call – that’s fixed in the code.

In summary, workflows excel when **each step of the task is known and the value of the LLM is in carrying out those steps with greater understanding or creativity**. They are common in enterprise scenarios where consistency and auditability are important, such as form-filling assistants, report generation with fixed format, content moderation pipelines, etc. Many early LLM applications in 2023–2024 adopted such patterns because they were easier to trust and evaluate.

**Agent Use Cases:** Agents shine in **complex or exploratory tasks** that involve a mix of reasoning, tool use, and possibly interaction. Notable examples include:

* *Coding assistants/agents:* The software development domain has embraced autonomous agents that can take a high-level ticket or bug description and attempt to write and modify code to implement a solution. Anthropic mentions a coding agent that can resolve tasks from a benchmark by editing multiple files based on a task description. Here, the agent may need to read and understand existing code, plan which files/functions to change, apply edits, run tests, and iterate if tests fail. The number of edit steps or files is not predetermined – making it a perfect use case for an agent rather than a static workflow. Code problems also have clear evaluation (tests pass or fail), providing ground truth feedback the agent can use at each step.
* *“Do Anything” assistants (general problem solvers):* If users can ask the AI to perform a wide range of tasks (answer questions, do calculations, fetch data from the web, manipulate files, etc.), an autonomous agent with a suite of tools is appropriate. For example, Anthropic’s *“computer use” reference implementation* lets Claude act like a user operating a computer – it can decide to open a browser, search for info, open a terminal, run commands, etc., all through tool APIs. No single workflow could enumerate all possible sequences for such an open scope; the agent must decide actions based on the situation.
* *Interactive customer support agent:* As discussed in the Anthropic article’s appendix, customer support is a promising area for agents. A support agent needs to handle open-ended dialogue, retrieve customer data or knowledge base info, execute actions like refunds or bookings, and clarify details with the customer. The flow of conversation can twist and turn unpredictably, so an agent framework allows the system to respond flexibly while combining conversational steps with tool-using steps. Success in this use case is measured by resolution of the issue, and the agent can be given leeway to try multiple approaches (ask questions, consult database, offer solutions) to reach that goal.
* *Research and analysis assistants:* Consider an AI agent tasked with writing an analysis on a given topic. It may need to search for sources, read and summarize them, draft a report, ask the user for clarification on objectives, and so forth. The agent essentially performs a research workflow but it decides when to stop searching, which references to follow, how to structure the writeup, etc. This is inherently agentic because the path isn’t fixed – it depends on what information is found and what the user’s high-level request is.
* *Iterative creative tasks:* For tasks like writing long-form content, designing something, or troubleshooting, agents can iterate and refine. For instance, an agent writing a story could draft a section, review its own writing for consistency, adjust characters or plot points, and continue – all without a preset number of iterations. A workflow could force a fixed number of drafts, but an agent can adapt the number of refinement cycles needed based on its own evaluation of the output quality (this aligns with the idea of an evaluator-optimizer loop being used in a flexible manner).

The common theme in agent use cases is the need for **adaptive, on-the-fly decision making** and often the integration of **conversation and action**. As Anthropic notes, agents add the most value for tasks that require both understanding complex inputs (often via conversation) and taking actions in an environment. They are especially useful when there are clear success criteria to optimize for (e.g. passing all tests for a coding agent, or achieving a successful customer resolution) and when a feedback loop is available (tests results, customer responses, etc.) to inform the agent’s next steps. In such cases, the agent can continuously improve its performance in real time by using the feedback, something static workflows can’t easily do beyond whatever was hard-coded.