From ReAct reasoning to multi-agent systems — the patterns that power production AI.
The ReAct pattern interleaves reasoning with action. Instead of blindly calling tools, the agent thinks about what it needs, acts, observes the result, and adjusts. This is how the best agents work.
search_web("AI healthcare impact 2025 studies statistics")search_web("AI healthcare risks bias clinical trials 2025")calculator("150000000000 * 0.7") — adjusting for adoption barrierssearch_web("AI healthcare")Before executing anything, the best agents plan first. They decompose complex tasks into discrete steps, then execute them one by one. Planning before acting dramatically reduces errors and wasted effort.
Search for direct and indirect competitors in the SaaS space. Compile a list of the top 5 by market share.
Plan Execute: search_web + analyzeFor each competitor, catalog their pricing, core features, and unique selling points from their websites and reviews.
Plan Execute: scrape_page x5Map each competitor on a price-vs-features matrix. Identify market gaps and positioning opportunities.
Plan Execute: analyze + calculateSWOT analysis for each competitor. Pull from user reviews, G2 ratings, and social sentiment.
Plan Execute: search_reviews + summarizeCombine all findings into a structured report with executive summary, comparison tables, and strategic recommendations.
Plan Execute: write_reportOne agent is powerful. But teams of agents — each with a specialized role — can tackle problems no single agent can handle. Here are three production patterns.
Like an assembly line. Each agent has one job and passes its output to the next specialist. Simple, predictable, easy to debug.
One agent proposes, another critiques, a third synthesizes. The tension between viewpoints produces stronger, more balanced outputs.
A manager agent analyzes the task, decides which specialists to invoke, coordinates their work, and assembles the final output. Most flexible pattern.
Each station does one thing well and passes the product forward. Toyota Production System meets AI.
Proposals get challenged by independent reviewers. Stronger outputs through constructive friction.
A PM evaluates the task, assembles the right team, delegates work, and delivers the final product.
We've been building toward this all semester. Each session added a new layer of capability. Here's the progression from simple tool use to autonomous agent networks.
LLM + Calculator + Stock Lookup. The model decides which tool to call.
LLM + Reasoning Loop + Research Tools. The model thinks, acts, and observes in cycles.
Teams of specialized agents coordinating on complex workflows.
Self-organizing agent swarms that discover, recruit, and coordinate other agents.
The Model Context Protocol (MCP) is an open standard that lets any AI model connect to any tool through a single, universal interface — just like USB-C replaced a drawer full of different cables.
Not every problem needs a multi-agent system. Use the simplest approach that gets the job done. Here's your decision guide.