The hardest part of building an AI agent system isn't choosing between GPT-4o and Claude 3.7. That decision takes about 20 minutes.
What actually kills these projects: chaining multiple steps together with external tools. A single agent handling a single task in a demo looks clean and capable. A workflow that needs to call an API, wait for a result, update a database, and hand off to a second agent based on what the first one found - that's where things go sideways.
Where State Management Actually Breaks Down
Language models (LLMs) don't retain anything between separate API calls. Each call is stateless by default - the model has no memory of step one when executing step three. You have to manually pass context along, deciding at each step what to include, what to summarize, and what to drop.
Get that balance wrong and you hit the context window limit (the maximum amount of text the model can process in a single call - GPT-4o handles about 128,000 tokens, or roughly a 300-page book's worth of text). Include too little context and the next agent lacks the information it needs. Either way, the workflow produces confidently wrong outputs.
The Tool Execution Problem
External tool calls - reading from a database, sending a Slack message, running a web search - fail silently more often than you'd expect. The model doesn't always detect that a tool call didn't work. Without explicit error-checking between steps, workflows continue with bad or missing data. The agent keeps going; it just fabricates what it doesn't have.
The practical fix: build confirmation checks between every tool call. Test each external integration in isolation before wiring it into the larger workflow. Log everything so failures have a clear origin point.
Start Smaller Than You Think
The instinct with multi-agent systems is to plan the full architecture upfront - five specialized agents coordinating through a central orchestrator. In practice, each additional agent is another failure point, another coordination layer to debug, another place context can go missing.
Start with one agent that does one thing reliably. Add a second only when the first is solid. The model you pick matters less than getting this sequencing right. Framework abstractions like LangChain or CrewAI solve the plumbing fast but can obscure what's breaking when something fails - know that tradeoff before you commit.