The "treat your context window like RAM" framing resonates. We've been running into this exact tension building agentic workflows with Claude Code - the more tools you make available, the worse selection accuracy gets, even with very capable models.
Curious what others have found: does the code-generation approach to tool calling (agent writes Python instead of picking from JSON schemas) actually hold up at scale? It seems elegant for composition, but I'd worry about hallucinated function names or incorrect arguments being harder to catch than a malformed structured call. With JSON schemas you at least get validation for free.
Also interested in the "use intelligence once to create automation that runs forever without intelligence" pattern for cron jobs. Has anyone found a good middle ground between fully scripted automations and full LLM-every-loop? The cost blowup they describe ($5k/month from a 5-minute cron) seems like it would kill most production deployments before they prove value.
Curious what others have found: does the code-generation approach to tool calling (agent writes Python instead of picking from JSON schemas) actually hold up at scale? It seems elegant for composition, but I'd worry about hallucinated function names or incorrect arguments being harder to catch than a malformed structured call. With JSON schemas you at least get validation for free.
Also interested in the "use intelligence once to create automation that runs forever without intelligence" pattern for cron jobs. Has anyone found a good middle ground between fully scripted automations and full LLM-every-loop? The cost blowup they describe ($5k/month from a 5-minute cron) seems like it would kill most production deployments before they prove value.