#Ai Agents
Resources on AI agents for business — autonomous software that handles tasks like prospecting, procurement routing, and quality monitoring without human intervention.
18 resources
AI Agent Production Deployment: What We Learned Shipping 3 Enterprise Agents
We have shipped 3 production AI agents — a contact centre quality monitor (scaled 50 to 80+ agents), a CRM lead scorer, and a manufacturing cost estimator. Here's what each cost, how long it took, and what surprised us.
AI Implementation Cost for Enterprise: What Pilots and Production Deployments Actually Cost in 2026
Enterprise AI pilots cost $15K-$50K. Production deployments run $60K-$200K+ depending on integration complexity and data infrastructure. Here's the cost breakdown by deployment type.
AI for Insurance Agencies — What Actually Works Beyond the Hype
AI works for insurance agencies in document processing, carrier matching, renewal prediction, and claims routing. Here's what each costs and when custom AI beats vendor tools.
AI for Law Firms — Document Intelligence and Beyond in 2026
AI in law firms delivers ROI in document review, contract analysis, and legal research. Here's what each costs, what vendor tools exist, and when custom AI makes sense.
AI for Construction — Estimating, Scheduling, and Quality in 2026
AI in construction works for cost estimation, schedule optimization, and visual quality inspection. Here's what vendor tools exist, what custom AI costs, and which approach fits.
AI for Manufacturing — Production Applications That Actually Work in 2026
AI in manufacturing works in production today for predictive maintenance, cost estimation, and quality inspection. Here's what each costs, how they work, and when custom AI beats off-the-shelf.
AI for Real Estate Fund Management — Portfolio Analytics and Investor Reporting
AI in real estate fund management automates property valuation, investor reporting, and deal screening. Here's what works, what it costs, and when custom AI beats vendor platforms.
AI Agent Platform Comparison 2026: Build vs Buy for Enterprise
AI agent platforms range from $50/month no-code tools to $200,000 custom builds. Here's how CrewAI, AutoGen, LangGraph, and custom development compare for enterprise production use in 2026.
AI SDK Comparison 2026: Vercel AI SDK vs LangChain vs OpenAI SDK
Vercel AI SDK fits streaming chat UIs, LangChain fits multi-step agent orchestration, OpenAI SDK fits direct model access. Here's the production comparison across performance, cost, and architecture fit.
What Is Model Context Protocol (MCP)? A Business Guide
Model Context Protocol (MCP) is an open standard that lets AI systems connect to business tools through one interface instead of custom integrations. Here's what it means for enterprise AI costs, timelines, and architecture decisions.
MCP Server for Enterprise: When and How to Build AI Data Connectors
Enterprise MCP servers bridge AI agents and business data through one protocol — replacing custom integrations that consume 40-60% of AI project budgets. Here's what they cost, how they work, and when to build one.
AI Agent vs RPA vs Workflow Automation: What Actually Works in 2026
RPA automates repetitive screen actions — clicking buttons, copying data between systems, filling forms. Workflow automation enforces business rules — if X happens, do Y. AI agents make decisions with incomplete information — reading documents, classifying intent, choosing next steps. Most enterprises need all three, but waste money applying the wrong tool to the wrong problem. This guide explains when each technology works, what it costs, and where the boundaries are.
What Is AI Model Routing? How Production Systems Use Tiered Models to Cut API Costs
AI model routing directs different tasks to different model tiers based on complexity — classification to cheap models, reasoning to expensive ones. It reduces API costs by 80–95% in production systems. Most development teams skip it because it requires more upfront architecture work.
How to Reduce AI API Costs in Production: The Architecture Problems Your Vendor Won't Fix
Most production AI systems cost 10–20x more than they should because the development team used one model for every task. The fix isn't prompt shortening or caching — it's re-architecting the AI layer with model routing, task decomposition, and tier-specific prompt engineering.
Platform Architecture for AI-Native Companies: What Your Engineering Team Needs to Get Right (2026)
AI-native companies need platform architecture that handles multi-tenancy, compliance, legacy integration, agent orchestration, and human-in-the-loop workflows from day one. Here's the architecture pattern that separates production-grade AI platforms from demo-grade prototypes.
AI-Native Service Companies: What They Are, What They Build, and What Engineering They Need (2026)
AI-native service companies replace human-delivered services — accounting, insurance brokerage, compliance auditing, healthcare admin — with AI platforms. Here's what they are, what they need to build, and why the engineering is harder than the demo suggests.
Custom Enterprise AI Chatbot: What It Includes, When to Build vs Buy (2026)
An enterprise AI chatbot handles employee queries, customer support requests, or sales qualification by retrieving answers from internal knowledge bases, CRM records, documentation, and structured data — rather than generating generic responses. The difference between a custom enterprise chatbot and an off-the-shelf deployment is the quality of the retrieval layer and the depth of system integration.
Machine Learning Software Development: What It Takes to Build Production ML Systems (2026)
Machine learning software development requires data pipelines, model training infrastructure, feature stores, and monitoring systems that demos never need. Here's what production ML actually takes to build and maintain.