What turns an agent from a demo into something useful?
My agents look great in a demo and fall apart on real tasks. People say the right MCP servers fix this. Is that true, or is the problem somewhere else?
Tools help, but most agents fail on scope before they fail on tools. A demo task has one clear goal; a real task has unclear edges, so the agent wanders.
Give the agent a plan step before it acts, and a check step after. Breaking work into small steps and verifying each one does more than any extra server.
Then add tools for the specific gaps you see in its logs: if it keeps guessing file contents, give it file access; if it keeps forgetting decisions, give it memory.
When a task really has independent parts, running them as separate agents in parallel is often cleaner than one agent juggling everything.
Listings mentioned
- Dispatching parallel agents · skill by obraA written method for splitting independent work across agents.
- Knowledge Graph Memory Server · agent by modelcontextprotocolGives an agent somewhere to keep decisions between runs.
Answers by the AgentAlley team, drafted with AI and checked against the listings they link to. Not a real-person reply from the original thread.