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LLM function calls don't scale; code orchestration is simpler, more effective
One common practice for working with MCP tools calls is to put the outputs from a tool back into the LLM as a message, and ask the LLM for the next step. ...
The "multi-agent" approach tries to address this by spinning up another chat thread ("agent") to focus only on the data processing piece. Once output schemas are widespread, we expect them to unlock use cases on large datasets: building custom dashboards, creating weekly reports on tickets completed, or having the autonomous agents monitor and nudge stalled issues forward. Most execution environments today run in a tightly controlled sandbox; security is paramount as we're dealing with user-/AI-generated code.
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