Practical Agent Patterns You Can Build Today

Agent technology is moving fast, and some patterns are already mature enough for reliable production use. This lesson focuses on what is practical now, not theoretical future capabilities.

Pattern 1: The Research Agent

A research agent takes a question or topic, researches it across multiple sources, synthesises the findings, and produces a structured report.

1. 1.What it does:
2. 2.Receives a research question
3. 3.Plans a set of search queries to gather relevant information
4. 4.Executes searches and reads results
5. 5.Identifies gaps and performs additional searches as needed
6. 6.Synthesises findings into a structured output

Tools needed: Web search, document reading, text generation

Current reliability: High for factual research on well-documented topics. Lower for cutting-edge or niche topics where training data is thin.

Where to build it: Perplexity is a consumer research agent. For custom implementations: LangChain, LlamaIndex, or direct API integration.

Pattern 2: The Code Agent

A code agent takes a specification or bug description, writes code to address it, tests the code, fixes failures, and returns working code.

1. 1.What it does:
2. 2.Receives a coding task or bug description
3. 3.Writes an initial implementation
4. 4.Runs tests or executes the code
5. 5.Observes failures and revises
6. 6.Iterates until tests pass or it determines the problem is outside its scope

Tools needed: Code execution environment, file system access, testing framework

Current reliability: Good for well-defined tasks with clear test criteria. Struggles with ambiguous requirements and novel algorithmic problems.

Where to build it: GitHub Copilot Workspace, Cursor Agent mode, Devin (professional), or DIY with the OpenAI API plus a sandboxed code execution environment.

Pattern 3: The Data Pipeline Agent

A data pipeline agent takes a data processing goal, writes and executes the code to achieve it, and returns the processed data or analysis.

1. 1.What it does:
2. 2.Receives a data transformation or analysis goal
3. 3.Examines the input data structure
4. 4.Writes processing code
5. 5.Executes and validates results
6. 6.Iterates on failures

Tools needed: Code execution, file I/O, database access

Current reliability: High for standard transformations and analyses. Requires careful validation of outputs.

Pattern 4: The Monitoring and Alert Agent

An agent that monitors a set of conditions and takes specified actions when thresholds are crossed.

1. 1.What it does:
2. 2.Runs on a schedule (hourly, daily)
3. 3.Checks defined conditions (new competitor pricing, metric thresholds, news mentions)
4. 4.Applies AI judgment to determine if conditions warrant action
5. 5.Takes defined actions (send alert, create ticket, draft report)

Tools needed: Web scraping or API access, database reads, communication tools

Current reliability: High for well-defined monitoring tasks with clear criteria.

Using Agent Frameworks

Rather than building agent infrastructure from scratch:

[LangChain / LangGraph](https://www.langchain.com) Most widely used Python framework for agent development. Good documentation, large community, extensive tool integrations.

[CrewAI](https://www.crewai.com) Higher-level framework focused on multi-agent collaboration. Easier to get started for multi-agent patterns.

[AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) Early open-source agent framework. Less production-ready but useful for experimentation.

[OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) Managed agent infrastructure from OpenAI. Handles memory, tool calling, and conversation management. Reduces infrastructure complexity.