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Practice / AI-AGENTS

Production AI agent development

LLM agents that resolve real tickets, route real tools, and survive a real eval suite. Vendor-agnostic over the major model providers.

Production AI agent development is the discipline of building LLM-driven systems that orchestrate real-world tools (APIs, queues, databases) under typed contracts, with observability, eval suites, billing meters, and on-call coverage. The agent is the orchestrator, not the product; it pays rent by resolving work that previously required a human queue.

Looking for an AI agent developer, a production LLM consultant, or an AI development agency that ships beyond the demo? Most agent demos do not survive contact with production. Ours do because we treat them like any other software system: typed contracts, instrumentation, billing meters, a reviewer loop, eval suites, on-call paging.

Our production AI agent development practice covers tool-use orchestration, prompt caching with reviewer loops, per-tenant billing meters, structured-output extraction, RAG pipelines, document classification and routing, model evaluation harnesses, and adjacent AI engineering. Vendor-agnostic by default across OpenAI, Anthropic Claude, Google Gemini, and on-prem models; we do not lock you into a single provider, and we explicitly avoid taking a position on whose foundation model is best for tomorrow.

The cases below cover production agents that pay rent, not demos that win conference talks.

Common questions

What is a production-grade LLM agent?
A production-grade LLM agent is a system where a language model orchestrates real-world tools (APIs, databases, queues) under typed contracts, with instrumentation, eval suites, billing meters, and on-call coverage. Unlike a demo, it survives bad inputs, partial outages, and adversarial users; it logs every tool call for audit; and it pays rent by resolving work that previously required a human queue. The agent is the orchestrator, not the product.
How is a tool-use agent different from a chat assistant?
A chat assistant answers questions; a tool-use agent takes actions. Tool-use means the model picks which API or function to call, in what order, with what arguments, then reads the result and decides next steps. Building one requires a typed tool catalog, parameter validation, retry and rollback semantics, observable traces, and a reviewer loop that catches bad calls before they ship a refund or send an email to the wrong customer.
What does vendor-agnostic mean for AI agent development?
Vendor-agnostic means the agent is portable across major model providers, not coupled to one. We abstract the model boundary behind a thin contract (chat, tools, JSON mode, vision, embeddings), so swapping providers is a config change, not a rewrite. This protects against price shifts, capability gaps, and vendor lock-in. We avoid features that exist in only one provider unless the buyer explicitly accepts the lock-in.
How do you measure quality on production agents?
Quality is measured by an eval suite of representative cases that runs on every change, plus production telemetry: tool-call success rate, retry rate, escalation-to-human rate, latency P95, cost per resolved ticket. We pair the eval suite with a reviewer loop where a smaller model (or a deterministic rule set) flags suspicious outputs before they ship. Without these you ship a demo, not a system.

Selected work in production ai agent development

CASE 05 / AI agents

Tool-use agent · production

Production LLM agent with prompt caching, tool routing, structured output, per-tenant billing meters, and a reviewer loop that catches bad calls before they ship. Vendor-agnostic abstraction over multiple model providers...

LLM · TypeScript · Postgres View case
CASE 06 / Healthcare · imaging

Medical document OCR and image classification

A production medical document OCR and image classification pipeline on .NET 10 and LeadTools. High-volume intake of scanned records, faxes, TIFF and PDF; deskew and image cleanup, document-type classification, optical ch...

.NET 10 · LeadTools · OpenCV · Tesseract · MSSQL View case

Tell us about the workflow your agent should resolve.

Triage, classification, structured extraction, tool routing, billing-aware multi-tenant. We respond within one business day with a written one-pager.

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