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Strip HTML to plain text before passing web content to agents to cut token costs by ~7x and reclaim context window space for content the model actually reasons over.
Practitioners building agentic systems should note that context discipline and software engineering fundamentals — not model selection — are what prevent complex AI-assisted projects from collapsing into unmaintainable code.
Developers building agentic systems should audit their error-handling paths to ensure that LLM call failures produce meaningful diagnostic memory entries — not just incremented counters — so agents can reason about and recover from outages rather than merely surviving them.
Teams running production AI agents with many MCP servers can cut token costs by over 50% — and up to 93% at scale — by switching to Code Mode without sacrificing task accuracy.