Rapid Acceleration Partners buying intent

𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝘄𝗮𝘀 𝗻𝗲𝘃𝗲𝗿 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗮𝘀. We kept tuning prompts. The model kept reasoning over the wrong material. Stale memory from three steps ago. Tool outputs the next step didn't need. Retrieved chunks that almost-but-don't-quite matched. The prompt looked clean. The context window was full of noise. So we changed how we thought about it. Context is infrastructure. Not a string. It has tiers. It has eviction rules. It has a quality score. It gets versioned, monitored, debugged. What that looks like for us: - Layered context, persistent, session, transient. Each with its own lifecycle. - Retrieval that knows when to stop. One good chunk beats ten mediocre ones. - Tool outputs summarized before they hit the main model. The big model never sees raw blobs. - Context logging as a first-class concern. Every decision traceable to the tokens that produced it. Prompt engineering is how we talk to the model. Context architecture is what the model actually sees when it decides. The second one is doing more of the work than we expected. Curious how others are handling this, what's the part of your context pipeline that broke first? #AI #EnterpriseAI #AIAgents #AgenticAI #AIInfrastructure #ContextEngineering #ResponsibleAI