What Are AI Agents?
AI agents are systems that use large language models (LLMs) to take actions. Unlike simple prompt-response setups, agents can understand context, make decisions, and use tools to handle complex tasks.
Here's how they work differently:
Workflows use LLMs to follow paths set by developers. The steps are fixed, even if the content changes.
Agents are more independent. They decide their own steps and tools to use, rather than sticking to a set script.
The main challenge is balancing flexibility (letting the model decide) with control (ensuring safe, predictable behavior).
Core Agent Patterns
Sequential Processing (Chains)
This pattern runs steps in a set order, with each step's output becoming the input for the next. It creates a straightforward chain of operations for tasks that follow a clear sequence.
Practical examples:
Content creation pipelines that outline, draft, and refine marketing materials
Data processing workflows that clean, analyze, and visualize datasets in stages
Customer application processes that gather information, verify eligibility, and produce documentation
Routing
In routing, the model sorts the input and sends it to specific processes. This way, it can handle different inputs that need different methods.
Practical examples:
Customer support systems that route queries to technical, billing, or general information specialists
Content moderation that directs different types of flagged content to appropriate review processes
Document processing systems that handle invoices, contracts, and forms with specialized workflows
Parallelization
This pattern runs independent tasks simultaneously and then combines their results. It comes in two forms: sectioning (breaking a task into parallel subtasks) and voting (running the same task multiple times for consensus).
Practical examples:
Code review systems that simultaneously check security, performance, and style
Content moderation where separate models evaluate different aspects (hate speech, explicit content, misinformation)
Market research analysis that processes different data sources in parallel before synthesis
Orchestrator-Worker
A central "orchestrator" LLM coordinates specialized "worker" LLMs. The orchestrator maintains overall context while workers handle specific subtasks.
Practical examples:
Software development systems where an architect model coordinates with specialist models for frontend, backend, and database code
Research assistants that delegate literature review, data analysis, and report writing to specialized components
Complex customer service scenarios where a coordinator manages specialists in technical support, policy, and account management
Evaluator-Optimizer
This pattern creates a feedback loop where one LLM generates content while another evaluates and provides feedback. This creates self-improving systems.
Practical examples:
Writing assistants that generate content and then have another model evaluate for quality, suggesting improvements
Translation systems that create translations and then check them for accuracy and cultural nuance
Data visualization workflows where one model creates charts and another evaluates their clarity and effectiveness
Multi-Step Tool Usage
This pattern allows the LLM to make multiple tool calls across steps without explicitly specifying the order. The model decides what tools to use and when based on the task.
Practical examples:
Research agents that search for information, analyze the results, and write summaries based on what they find
Productivity assistants that schedule meetings, check calendars, and send notifications as needed
Data analysis workflows where the agent queries databases, performs calculations, and creates visualizations
When to Use Agents vs. Simpler Approaches
The main idea is to keep things simple. Agents can be complex, slow, and costly, so only use them when necessary. In many cases, a single, well-tuned LLM call with retrieval does the job just fine.
Consider agents when:
Tasks require multiple steps with decisions between them
The problem is open-ended with an unpredictable path to solution
Different types of expertise or processing are needed at different stages
Quality control and feedback loops would significantly improve outcomes