AI Agent Development

The distance between a functional AI agent demonstration and a production-grade agent is substantial, and most enterprise AI pilots stall somewhere inside that distance. The systems that reach production reliably share a common architecture: disciplined tool use integration with systems of record, retrieval infrastructure graded against real user queries, evaluation suites that gate releases, human-in-the-loop design where the agent's confidence warrants escalation, and operational observability sufficient to detect drift and cost anomalies before they reach users.

We build agents that meet that standard. The engagement model brings strategy, engineering, and evaluation expertise into a single team that owns the agent from initial scoping through production deployment and operational handoff. Engagements span internal knowledge agents, operational automation agents, customer-facing conversational agents, and domain-specific systems for underwriting, claims processing, document analysis, and similar workflows. The team is model-agnostic and builds on the stack most appropriate to the workload and the client's existing technology posture.

Our work covers:

• Agent architecture design including model selection, orchestration, and multi-model coordination
• Tool use and API integration with systems of record including CRM, ERP, ticketing, and custom platforms
• Retrieval infrastructure including document ingestion, embedding strategy, vector storage, and hybrid search
• Evaluation framework including task-level evals, regression suites, and failure-mode testing
• Human-in-the-loop design including confidence thresholds and supervisor review workflows
• Production deployment including observability, cost monitoring, safety guardrails, and incident response
• Operational handoff including runbooks, dashboards, and on-call structure for ongoing ownership