What is an AI agent, and how is it different from a workflow?
Key facts
- Anthropic defines workflows as systems where models follow predetermined code paths, whereas agents dynamically guide their own processes.
- Agentic systems trade off execution speed and operational expense to achieve improved task performance.
- Workflows provide predictable execution for structured tasks, while agents offer flexibility for situations requiring model-guided choices at scale.
- The primary foundation of an agentic architecture is a language model augmented with tools, memory, and retrieval mechanisms.
- Agents rely on environmental feedback, such as tool outputs and code results, to verify their progress during execution.
Architectural Definitions of Workflows and Agents
Anthropic groups systems that use language models and tools under the term agentic systems. Within this group, workflows represent architectures where language models and tools follow predetermined code instructions. In contrast, agents describe implementations where models independently control their processes, choose tool usage, and dictate how tasks are executed.
The Augmented Language Model Foundation
The fundamental building block of an agentic setup is an augmented language model. This architecture enhances the model with external tools, memory, and retrieval systems. According to Anthropic, modern models can generate search queries, select relevant tools, and decide what context to preserve.
Common Workflow Architectures
Prompt chaining breaks a task into fixed sequential steps where each model call processes the output of the preceding call. Anthropic notes that this pattern trades latency for increased accuracy by turning complex goals into simpler subtasks. Programmatic checks can be added at intermediate steps to confirm results before proceeding.
Routing workflows categorize input requests and direct them to specialized prompts, tools, or downstream processes. This pattern enables separation of concerns across different model calls. Without routing, tuning a single prompt for one type of input can degrade performance on other requests.
Other common patterns include orchestrator-workers and evaluator-optimizer workflows. In an orchestrator-workers design, a central model dynamically determines subtasks and delegates them to worker models before combining the findings. In an evaluator-optimizer workflow, one model generates a draft while another model provides feedback in an iterative cycle.
How Autonomous Agents Operate
AI agents begin execution upon receiving user instructions or through an interactive discussion. After clarifying the task, the agent plans and carries out actions independently, returning to the user if it requires more details. The agent can pause execution at preset checkpoints or whenever it encounters blockers.
According to Anthropic, agents are fundamentally language models operating tools within an environmental feedback loop. Tasks usually conclude when completed, but systems frequently incorporate explicit stopping rules, such as maximum iteration limits. These stopping rules help operators preserve control over agent execution.
Choosing Between Workflows and Agents
Anthropic states that many applications do not need agentic architectures, as single model calls with retrieval and examples are often adequate. When more structure is required, workflows offer predictability and consistency for well-defined problems. Agents are suitable for open-ended tasks where required actions cannot be determined in advance.
Deploying autonomous agents introduces higher execution costs and risks related to compounding errors across multiple turns. Anthropic recommends running tests in sandboxed environments and using guardrails to manage these risks. Testing tool usage thoroughly helps avoid operational mistakes during execution.
Practical Applications of Agentic Systems
Anthropic identifies customer support and software engineering as two primary domains for agentic implementations. Customer support combines natural conversational flows with programmatic tool use, such as looking up order history or processing refunds. Success in customer service can be evaluated through defined resolutions.
In software development, agents can solve coding tasks because proposed solutions are verifiable via automated test suites. Agents can use test feedback to modify their code across multiple attempts. Anthropic reports implementing coding agents capable of editing multiple files to address software benchmark tasks.