What is context engineering for AI agents?

Summary

• Context engineering is the discipline of designing, curating, and delivering the right information to AI systems at the right time so they reason about your business correctly.
• Without proper business context, even advanced AI models produce incomplete, inaccurate, or inconsistent results because they don't know how your organization operates.
• Effective context engineering includes system prompts, retrieval systems, memory architectures, tool integration, and governance to ensure only high-quality, authorized data enters the agent's context.
• Genie Ontology automates context engineering by continuously extracting knowledge from your Lakehouse, queries, dashboards, and metrics to build a living business context graph.
• The best approach is to model your most critical concepts deliberately (through Unity Catalog semantics), then let AI systems automatically learn the broader context from existing assets.

What is context engineering for AI agents?
Context engineering is the discipline of designing, curating, and delivering the right information to AI systems at the right time. The core problem is simple but profound: large language models know how to reason, but they don't know your business. Without proper context, even the most advanced models produce incomplete, inaccurate, or inconsistent results. Context engineering solves that by building the semantic environment that lets AI agents reason correctly over your data and operations.

Why context matters for AI agents

Think of an AI agent like a new employee on their first day. Give them access to your database and they can read the data, but they won't understand what it means or why it matters. They don't know that a "customer_id" sometimes refers to a primary account and sometimes a billing alias. They don't understand which metrics matter most to your business or what tradeoffs are acceptable. That knowledge gap is what context engineering closes.
The semantic layer is the bridge between technical structure and business meaning. A database schema tells systems what fields exist; semantics tell both humans and machines what those fields mean, when to use them, and what actions they support. Context engineering systematically builds that semantic understanding.

Key dimensions of context engineering

Dimension	What it means
System prompts and instructions	Structuring effective guidance so agents behave predictably and follow your business rules.
Retrieval and ranking	Surfacing the most relevant knowledge at inference time, ranked by authority and freshness.
Memory and state	Persisting and reusing state across sessions so agents build on previous interactions.
Tool integration	Connecting agents to external data sources and systems via protocols like Model Context Protocol (MCP).
Governance	Ensuring only high-quality, policy-compliant data enters the agent's context, with proper access controls and PII handling.
Context window management	Applying compaction and trimming strategies so agents stay efficient without losing critical information.

How Genie Ontology approaches context engineering

Genie Ontology automates context engineering by building a living business context graph from your existing assets. Rather than asking data teams to manually maintain a separate semantic layer, Genie continuously extracts knowledge snippets from your Lakehouse tables, SQL queries, dashboards, metrics, and notebooks. The system organizes these snippets into a graph that AI agents can reason over.
The architecture is two-layered. The modeled layer captures deliberately curated business semantics from Unity Catalog, including Metric Views, Domains, and your glossary. The inferred layer automatically extracts broader context from everything else: how tables relate, what metrics depend on what data, how teams actually use the system. This inferred context refreshes weekly and captures the relationships that matter most to your business.
Genie Ontology determines what's authoritative using an algorithm similar to PageRank, weighing where a definition came from, the author's expertise, how often people rely on it, how closely it ties to certified assets, and how current it is. It then enforces permissions by only showing content users are already authorized for. This means agents inherit your organization's access controls automatically: an analyst only sees insights they're already permitted to see.

The philosophy: model the head, infer the tail

Genie follows a deliberate design principle: organizations define their most critical concepts explicitly through Unity Catalog semantics, then let Genie automatically learn the broader context from existing assets. This hybrid approach gives AI agents a much richer understanding than a hand-built semantic model alone, while keeping curation effort proportional to actual business value.
Genie Ontology powers both Genie One (the AI coworker interface) and Genie Agents (domain-specific agents you build). External agents can also access Genie's intelligence through MCP. The result is a unified, adaptive enterprise context layer that continuously improves as teams use it, without requiring manual ontology maintenance.

FAQs

How is context engineering different from prompt engineering?

Prompt engineering focuses on how you phrase a single request to an AI model. Context engineering is broader: it's about systematically designing the information environment so agents reason correctly across many requests, over time, as requirements change. A good prompt helps; good context is essential.

Can I build context engineering without Genie Ontology?

Yes. You can manually curate a semantic layer, write detailed system prompts, and implement retrieval systems. But it's labor-intensive and hard to keep in sync as your business evolves. Genie Ontology automates this by learning from your existing assets, so you get the benefits without constant maintenance.

What happens if my business context changes?

Genie Ontology's inferred layer refreshes weekly, so it naturally picks up changes as teams update queries, dashboards, and metrics. Your modeled layer (the deliberately curated concepts) stays stable unless you change it, which means agents get consistent guidance on what matters while still learning about new developments.

How does context engineering affect the cost of running AI agents?

Good context engineering actually reduces cost. When agents have the right context, they make better decisions faster, need fewer retries, and use fewer tokens overall. Genie Ontology's automatic extraction means you get rich context without paying data teams to build it manually.