Better Context Engineering With Context Shells
The discourse around "context engineering" is heating up. Engineers building multi-agent systems are grappling with the same fundamental challenge: how do you get the right information to the right agent at the right time?
We've recently seen three context engineering approaches emerge as best practices:
- Offload to external stores — Move context out of the prompt and into retrievable systems (vector DBs, knowledge graphs, structured stores)
- Reduce via summarization — Compress context to maximize signal per token
- Isolate with sub-agents — Scope each agent's context to only what it needs for its specific task
These are solid patterns. They work. But they all share an unstated assumption: that you already have good context to manage.
But what if you don't?
The Missing Layer
Most context engineering conversations start downstream at the agent orchestration layer. How do we route? How do we compress? How do we scope?
But orchestration can only work with what it's given. And what most agent systems are given is... a mess. Raw web scrapes. Stale CRM data. Whatever the last person copy-pasted into Notion.
This is the Layer 0 problem: before you can orchestrate context, you need to generate it.
Not retrieve it. Not summarize it. Generate structured, queryable, agent-ready intelligence from primary sources.
One URL. Structured Context. Two Minutes.
At Strata, we built [context infrastructure] (https://getstrata.ai/blog/what-is-context-infrastructure) that transforms a single company URL into a context shell - a comprehensive strategic intelligence package that includes competitive positioning, market dynamics, ICP analysis, and decision-relevant frameworks.
What comes back via the API isn't a document dump. It's structured, sectioned intelligence designed to slot directly into agent workflows.
How Context Shells Power All Three Orchestration Patterns
Here's where Layer 0 meets orchestration. Each of the three orchestration approaches works better when the input context is already clean:
1. External Stores: Strata as Canonical Source
The "offload to external stores" pattern assumes you have a reliable source to query. Most teams cobble this together from web scraping, enrichment APIs, and manual research — creating maintenance nightmares and stale data.
Context Shells become that canonical source. Instead of agents doing their own scattered research, they query Strata. One source of truth. Always current. No retrieval pipeline to maintain.
2. Summarization: Pre-Distilled Intelligence
The summarization pattern fights token bloat by compressing raw inputs. But summarization is lossy, and you're trusting the compression step to preserve what matters.
Context Shells arrive pre-distilled. We're not handing agents raw web pages to summarize. We're handing them structured intelligence where the reduction already happened upstream, with domain-aware extraction that knows what matters for strategic decisions.
The difference: your agents spend tokens on reasoning, not on figuring out what's relevant.
3. Sub-Agent Isolation: Scoped Sections
The isolation pattern gives each sub-agent only the context it needs. But this requires context that's structured for scoping - not a monolithic blob.
Context Shells have distinct sections precisely for this purpose, so that each agent sees exactly what it needs. No prompt stuffing. No attention dilution.
Layer 0 is Infrastructure
We're not building another orchestration framework. Orchestration tools are good and getting better.
We're building what goes under orchestration. Strata context shells are the foundational context layer that makes everything above it work. Infrastructure, not application.
Because every agent workflow starts the same way: "Wait, what do we actually know about this company?"
Layer 0 answers that question before your agents have to ask.
Strata transforms company URLs into structured strategic intelligence in under two minutes. We're building context infrastructure for the agentic age.
Get early API access at getstrata.ai