The author introduces Weavable, a platform layer built to address context pollution and persistence in AI agent workflows by preprocessing data from enterprise tools before passing it to LLMs.
If you have been building agent workflows that rely on actual business context (from the tools that you already use), you have probably faced some level of unreliability issues if not complete agent breakdown. We have been playing with a lot of options including just connecting apps to Gumloop and Claude and so on, but while the answers work ok for summary snapshots, a lot is left on the table for doing real analysis that leads to measurable outcomes. Think of flows from outbound to pipeline reviews to eng roadmap planning and execution. So we built Weavable. We think that any successful agent needs to build a layer that continuously tracks changes across work, synthesizes and makes sense of them, allows you to sufficiently reason and drill down into cause and effect without burning through your entire token budget or dumping raw API polls into your LLMs forcing them to reason afresh every single time there is a query, multiplifed by the number of instances across the team. Moreover in the enterprise context, you are usually having to deal with permissioning, tenant management and ensuring that users don't end up seeing something they are not supposed to. Weavable is that layer. It sits underneath your tool stack, pre-processes and scopes context from HubSpot, Slack, Jira, Notion and more, and serves it to Claude, ChatGPT, Cursor or any agent through a single MCP endpoint. Would love to hear what you have had success with, or even war stories of workflows that didn't exactly function the way they were meant to and if you managed to figure out what the bottlenecks were. Bonus points for pointing out if something like what we built might unlock that gnarly agent workflow that has been blocking you.
The article describes lessons learned from building a 'harness' system to wrap coding agents with context, tools, provenance, and verification, detailing the first two of eight pillars: Context and Provenance.
This paper evaluates context engineering configurations for LLM agents in enterprise tool-use workflows, showing that summarization with selective pruning achieves 91.6% accuracy while reducing token usage by over 60% compared to full-context baselines.
Anthropic publishes a guide defining context engineering as the evolution of prompt engineering, focusing on curating optimal context tokens for AI agents to maintain performance and focus during multi-turn inference.
The article discusses the emerging pattern of 'wiki memory' for AI agents, where raw source data is intelligently compressed into a persistent, structured knowledge layer that agents can use efficiently. It compares this to basic RAG and gives examples like DeepWiki and LLM Wiki.