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We've deployed 40+ production agentic systems. This page covers everything you need to know — from architecture to failure modes to real deployment results.
Quick Summary
AI Agent: An AI agent is an LLM-based autonomous system that takes an objective, reasons about how to achieve it, calls external tools (APIs, databases, search engines), observes the results, adjusts its approach, and iterates — without per-step human instruction. Unlike a chatbot, an agent operates across many steps toward a goal and can take real-world actions.
Every production-grade enterprise agent system we build uses the same layered architecture. Skipping any layer causes specific, predictable failure modes.
User-facing interface — chat UI, API endpoint, embedded widget, or automation trigger. Should be thin; all intelligence lives below.
The agent brain — handles planning, tool selection, step sequencing, and state management. LangGraph for stateful workflows; direct orchestration for performance-critical systems.
The agent's hands — every external capability is a tool with strict input/output schemas. Every tool call is validated and logged.
Routes every LLM call with model selection, rate limiting, semantic caching, retry logic, and cost tracking. Critical for enterprise reliability.
Data and observability infrastructure — vector databases, structured databases, audit logs, monitoring.
These are not best practices — they're the minimum for any agent system running on real data with real consequences.
Every production agent failure clusters into one of these five patterns. Know them before you build.
Answers to the most common questions about building and deploying AI agents in enterprise environments.
An AI agent is an LLM-based system that takes an objective, reasons about how to achieve it, calls external tools (APIs, databases, search engines), observes the results, and decides on next steps — autonomously, without step-by-step human instruction. Unlike a simple chatbot that responds to a single query, an agent operates across multiple steps toward a goal.
A chatbot is request-response: one query in, one answer out. An AI agent is goal-oriented: it receives an objective, plans, calls tools, observes results, replans, and iterates until the goal is achieved or a limit is reached. Agents can write code, browse the web, query databases, send emails, and trigger other systems — not just generate text.
Insurance claims routing and data extraction, contract review and risk flagging, market research pipelines, customer onboarding orchestration, compliance monitoring, procurement analysis, support ticket resolution, code review and security scanning, and report generation from multiple data sources.
Through hard step limits (the agent cannot run indefinitely), tool call validation (every parameter validated before execution), human-in-the-loop gates (approval required before irreversible actions like sending emails or updating production records), immutable audit trails (every action logged), and graceful degradation (defined behavior for every failure mode).
LangGraph for stateful, multi-step workflows with human-in-the-loop gates and checkpoint/resume capability. LangChain for simpler RAG pipelines. AutoGen for multi-agent coordination tasks. Direct API orchestration for high-performance systems where framework overhead matters.
A single-agent system for a specific workflow: 4-8 weeks for a production-ready POC. A multi-agent system with human gates, monitoring, and security controls: 2-4 months. Enterprise-grade agent platform with multiple workflows: 4-12 months.