The Agent Landscape Has Matured
Two years ago, "AI agent" meant a ReAct loop with a few tool calls. Today, the space has exploded — and with it, the number of architectural choices.
After building and shipping agents across insurance, legal, sales, and research domains, here's our honest take on what works.
ReAct: Still the Baseline
The Reasoning + Acting loop is still the simplest effective pattern for single-task agents:
- Think: What do I need to do?
- Act: Call a tool
- Observe: What did the tool return?
- Repeat until done
It's reliable, debuggable, and works well for tasks with clear success criteria.
When it breaks down: Multi-step tasks requiring backtracking, tasks needing parallel execution, or anything requiring persistent state across sessions.
LangGraph: The Right Abstraction for Complex Agents
For anything beyond simple ReAct, we default to LangGraph. It models agent behavior as a directed graph where:
- Nodes are actions (LLM calls, tool invocations, conditional checks)
- Edges are transitions (including conditional routing)
- State is explicitly typed and persisted
This gives you observability, retry logic, and human-in-the-loop checkpoints for free.
Multi-Agent Systems: When to Use Them
Multi-agent systems shine when:
- Your task is parallelizable (research + writing + fact-checking can run concurrently)
- You need specialization (a legal-domain agent + a financial-domain agent)
- Tasks exceed single-context limits
They add complexity: you need orchestration, message passing, error handling across agents, and state synchronization.
Our rule: default to a single agent. Only reach for multi-agent when you hit a concrete limitation.
What We're Watching
Memory systems are the next frontier. Current agents are stateless between sessions. Long-term memory (episodic, semantic, procedural) will unlock genuinely useful personal AI assistants.
Computer Use (Claude's ability to control UIs) is opening up automation of software that has no API. Still early but extremely promising.
The field moves fast. The fundamentals — clear task specification, good tool design, reliable evaluation — stay constant.