StrataInsights

Beyond RAG: The External Context Layer AI Agents Need

How Strata builds golden context for agent reasoning about external entities

AI agents are becoming more proficient at working with internal knowledge. RAG systems retrieve from documentation and wikis. Memory layers track conversation history and user preferences. The infrastructure for internal context is maturing.

But most agent tasks require understanding entities outside the organization's knowledge base. Analyzing a competitor. Evaluating a potential partner. Researching a company before a sales engagement. Assessing an unfamiliar market. These tasks require agents to reason about external companies using context that internal documents simply do not contain.

This is the gap teams discover too late: their agents can retrieve internal knowledge, follow instructions, and maintain conversational continuity. What they lack is any mechanism for understanding the external entities central to their reasoning.

The Current State: Internal Context Infrastructure

RAG (Retrieval-Augmented Generation) retrieves relevant passages from internal documents when an LLM requires grounding. It enables agents to access wikis, policies, and institutional knowledge. Solutions like Pinecone, LlamaCloud, and LangChain serve this layer well.

Memory and conversation history provide agents with continuity as it tracks session context, user preferences, and prior decisions across interactions.

These systems address internal context effectively. However, neither helps when an agent needs to understand a company it has never encountered, drawing on information the organization has never documented.

The Missing Layer: External Entity Context

When agents require external context today, teams rely on inadequate workarounds:

Web scraping produces raw data, not structured understanding. Injecting a company's website into a prompt generates noise, forcing the agent to perform qualitative synthesis it is poorly equipped to handle (is a random Reddit comment the defining feedback on your enterprise software sector? Probably not!).

Search APIs return snippets optimized for human click-through, not for agent reasoning over structured facts.

Manual research delivers quality but cannot scale. It lacks consistency of treatment across searches. If every agent task requires human pre-research on relevant entities, the operational bottleneck remains intact.

Competitive intelligence platforms serve human analysts in well-resourced organizations whose entire job is monitoring dashboards over time. These platforms do not serve up structured context to agents for immediate reasoning.

The result: agents either operate blind to external entities, or receive floods of raw web content that obscure rather than clarify. Neither approach produces reliable outcomes.

The Golden Context Problem

Most agent context failures stem from two conditions:

Context starvation: The agent lacks information about the external entity entirely. It analyzes a competitive landscape without understanding any of the companies within it.

Context overload: The agent receives everything tangentially related - raw scrapes, unstructured data dumps, dozens of loosely relevant chunks. Signal is buried in noise. Token costs accumulate without corresponding quality gains.

What agents require is golden context : the minimal, decisive information that makes accurate reasoning nearly inevitable. Structured. Pre-synthesized. Containing precisely what the agent needs - nothing more.

What Strata Builds

The need is clear: agents will increasingly require infrastructure for generating structured, golden context about external entities, on demand, in formats optimized for agent consumption. Strata builds this information in the form of context shells. Context shells are structured strategic profiles containing the relevant, recent competitive context about the company and its market dynamics that an agent needs to reason intelligently.

Business model. Competitive positioning. Recent developments. Market dynamics. Strategic trajectory. Synthesized into formats optimized for agent reasoning rather than human review. Pre-scored, pre-structured, pre-compressed.

If you are building agents for GTM use cases (sales research, competitive analysis, market intelligence, or account planning), your agents need to reason about external companies. That requires structured external context, generated on demand, at scale.

Strata provides the external context layer these agents require.

Get started to see context shells in action.