• Context engineering for AI: what it is & how to build it
• Why it's important to monitor latency in AI networks
• Context assembly: building the prompt the model actually sees
• Model Context Protocol (MCP) vs. Agent2Agent (A2A): which protocol do you need?
• Dynamic chunking for RAG: building context infrastructure that adapts

Token prices have fallen roughly 280x over the past two years, yet enterprise AI spend has risen over 300%. The gap comes down to how tokens are used, not what they cost. Agentic workflows trigger 10–20 LLM calls per task, RAG expands context windows by 3–5x, and background agents keep models running long after the user is gone. Cheaper tokens didn't lower AI bills—they encouraged more model calls, longer prompts, and increasingly complex workflows.

One way teams are responding is semantic caching. Rather than sending every similar prompt back to the model, Redis LangCache recognizes near-duplicate requests and serves cached responses instead. Production deployments have reduced LLM spend by up to 70%, cutting costs without changing models or app architecture.

Redis LangCache brings semantic caching to production AI apps, reducing LLM spend by up to 70% without requiring changes to your models or app architecture.

95% of AI pilots never make it to production, and MIT's NANDA Initiative traced most of those failures to bad context, not weak models. Gartner expects 40% of enterprise apps to ship with task-specific agents by year end, McKinsey has 88% of orgs using AI in production, and the same story keeps repeating: the agent worked in the demo, then forgot the customer by Monday.

Teams building memory in-house are hitting what one recent post-mortem called "the month-two wall," an infrastructure problem that only shows up after real traffic, not real testing. That's the gap Redis Agent Memory is built for: automatic fact extraction, no duplicate memories, and a client that drops into LangGraph, the OpenAI Agents SDK, or Microsoft's Agent Framework without a new vendor or a new on-call rotation.