Beyond the single prompt
A chat model answers questions. An agent pursues goals: it decomposes an objective into steps, chooses tools, takes actions in real systems, and checks the result. Most enterprise value lives in that gap — and crossing it reliably is an engineering problem, not a prompting trick.
Specialized agents beat one generalist
For complex domains we orchestrate a set of specialized agents behind a router rather than asking one model to do everything. Our legal platform, for example, routes each matter across distinct research, drafting, clause-analysis, compliance, and litigation agents.
Specialization makes each agent easier to prompt, test, and improve in isolation — and the orchestrator's routing decision becomes an auditable record of how a result was produced.
Tool use, safely
Agents earn their keep by acting in your systems through function calling and well-typed tools. The danger is obvious, so safety is designed in, not bolted on. Our natural-language-to-SQL data agent is a good illustration:
- Schema introspection — the agent discovers your data model dynamically rather than relying on hardcoded queries.
- Read-only by construction — it can answer questions but cannot modify data.
- Multi-tenant isolation — every query is scoped to the caller's tenant, enforced at a single auth boundary.
- Full audit trail — every action is logged with cost and latency for review.
Verification and human-in-the-loop
Autonomy without verification is just faster mistakes. High-stakes steps pass through configurable approval gates, and agents are prompted to check their own output — validating extracted fields, re-running arithmetic, and citing sources — before anything is committed.
The goal is not to remove people; it is to let people supervise volume. Humans review exceptions and approve consequential actions while the agent handles the routine work end to end.
Graceful degradation
Production systems fail in boring ways — an API is down, a key is missing, a model times out. Our agents degrade gracefully: when an AI step is unavailable, deterministic fallbacks keep the workflow moving and clearly flag that a fallback was used, so reliability never depends on every external call succeeding.