Domain Specific Subagents Data

Domain-specific subagents are scoped agents that each handle one business domain — finance, product, ops — instead of one generalist agent trying to answer everything. They produce dramatically higher accuracy because context stays tight.

A single generalist agent trying to know everything about every domain is worse than three scoped agents each knowing their domain well. The reason is context bloat: a generalist has to carry all definitions for all terms, and ambiguity between domains destroys accuracy. This guide covers when to split, how to route, and how to share context across subagents. Related: progressive context disclosure and AI for data infrastructure.

Why One Big Agent Fails

A generalist agent has to load the union of all domain context into every prompt. Finance wants revenue recognized with GAAP rules. Product wants revenue booked on sign date. Ops wants revenue as cash collected. Put them all in one context and the agent has to pick, and without strong scoping signals it picks wrong.

Splitting into subagents means each one only sees its domain's definitions. The finance subagent never thinks about booked revenue because it was never in its context. Accuracy per subagent is higher, latency is lower (smaller context), and debugging is easier (clear ownership).

How to Split

Split by the boundaries your business already uses. Finance is a domain because finance has its own team and its own definitions. Product is a domain because product owns its own metrics. Ops is a domain because ops has its own tables and SLAs. The rule: if two groups use different definitions for the same term, they are different domains.

• •Finance subagent — revenue, ARR, margin, cash flow
• •Product subagent — active users, engagement, feature adoption
• •Ops subagent — uptime, incidents, performance
• •Growth subagent — acquisition, funnel, conversion
• •Sales subagent — pipeline, bookings, quota attainment
• •HR subagent — headcount, attrition, comp

Routing Between Subagents

An incoming question needs to be routed to the right subagent. A classifier looks at the question and the asker's team and picks the domain. If ambiguous, the router asks the user which domain they mean or dispatches to multiple and returns both answers.

The router is simple — a small LLM classifier or even a rule-based system works. What matters is consistency: the same question always routes the same way so users can trust the output. Surprises in routing are a common source of frustration.

Shared Context Across Subagents

Some context must be shared. Fiscal calendar, organizational hierarchy, security policies — these apply to every domain. The shared layer sits underneath the domain layers and is retrieved by every subagent. Updates to shared context propagate everywhere automatically.

Shared context is small — kilobytes, not megabytes. It is also slow-changing, which makes caching easy. The split between shared and domain-specific is one of the cleanest architecture decisions in agent design.

Cross-Domain Questions

Some questions span domains: how does marketing spend correlate with revenue. The router has to recognize these and orchestrate multiple subagents. The orchestrator dispatches subqueries (marketing subagent for spend, finance subagent for revenue), joins the results, and returns a unified answer. This is where agent frameworks earn their keep.

Ownership and Accountability

Each subagent has an owning team responsible for its context quality. Finance owns the finance subagent's glossary, corrections log, and tests. Product owns the product subagent's. This ownership model means domain experts actually contribute because they see the impact of their work on their own subagent.

Common Mistakes

The worst mistake is a single generalist agent for a large organization. The second is splitting too fine — one subagent per table is overkill. The third is no shared context layer, which means every subagent reinvents fiscal calendar. The fourth is a router that is not consistent across retries.

Data Workers ships domain-specific subagents out of the box with routing, shared context, and cross-domain orchestration. Teams assign each domain to its own owner and see accuracy jump within weeks. To see it running, book a demo.

Measuring Subagent Accuracy

Each subagent gets its own benchmark. Finance subagent is measured against finance questions; product subagent against product questions. Comparing across subagents is meaningless because they answer different kinds of questions, but comparing each one over time is the key reliability signal.

The measurement also drives ownership. Each domain team sees the accuracy of its own subagent, tracks it over time, and invests in improvements when it regresses. Without per-subagent benchmarks, accuracy is a central team problem, and domain teams feel no responsibility. With them, domain teams own their own quality.

A good rollout pattern is to start with one subagent, prove the model works, and expand to others as demand dictates. Starting with finance is common because finance questions are high-value and the glossary already exists. Product usually comes second. By the time you have three subagents, the pattern is clear and new domains plug in quickly.

Cross-Domain Debugging

Cross-domain questions are the hardest to debug. A question about marketing ROI touches marketing spend, product engagement, and finance revenue — three subagents, three contexts, three possible failure points. When the answer is wrong, the debugger has to trace across all three and find which one produced the error.

The trace format must support cross-agent traversal. A trace from the orchestrator should show which subagent it called, what each one returned, and how it merged them. Without that structure, cross-domain debugging becomes impossible and teams default to avoiding cross-domain questions altogether.

Data Workers structures traces to support cross-agent traversal natively. Every orchestrated question produces a single unified trace with sub-traces per subagent call. Debugging cross-domain questions takes minutes instead of hours and the subagent pattern stays viable at scale.

The organizational benefit of domain-specific subagents extends beyond accuracy. Each domain team owns its subagent context — the glossary entries, the canonical tables, the corrections — which means ownership aligns with expertise. Finance maintains the finance context, product maintains the product context, and nobody has to be an expert in everything. This ownership model scales with the organization instead of bottlenecking on a central team that cannot keep up with every domain change.

Domain-specific subagents are the scaling answer for complex organizations. Split along business boundaries, share the common context, orchestrate for cross-domain questions, and accuracy stays high as the warehouse grows.