How to keep AI agents from running out of control in production
10/31/2026 — 11/02, 17:23·2 sources (all)·2 reports (all)
Key takeaways
- A newer piece uses an Insights Agent on Mattrx, orchestrated with LangGraph and traced with Langfuse, to map what agents need before reachin…
Latest turnUsing the Insights agent on his Mattrx analytics platform, orchestrated with LangGraph and traced with Langfuse, the author breaks down what separates a demo agent from a production one: the loop, planning, tool use, memory, reflection, and guardrails. Typical failures include looping on ambiguous tasks, calling tools that don't exist, burning tokens on a single question, and taking destructive write actions. The advice: give the agent only the capability it needs for the next decision.
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Using the Insights agent on his Mattrx analytics platform, orchestrated with LangGraph and traced with Langfuse, the author breaks down what separates a demo agent from a production one: the loop, planning, tool use, memory, reflection, and guardrails. Typical failures include looping on ambiguous tasks, calling tools that don't exist, burning tokens on a single question, and taking destructive write actions. The advice: give the agent only the capability it needs for the next decision.
DEV Community · AIAI score 61
An agent doesn't run tools itself: the model issues the call and the application executes it. This long-form post walks through the full path from a single Function Calling round to production-grade tool orchestration, with runnable code and a symptom-cause-fix troubleshooting table covering tool descriptions and parameter validation.
掘金AI score 52
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