AI & Agents
Guides on agentic AI, AI agents, agentic workflows, and production AI systems for business.
20 resources
AI Agent Development: How Production Agents Actually Get Built (2026)
Building an AI agent for production requires five things before writing code: a scoped task, accessible data, success criteria, a human escalation path, and a monitoring plan. Most AI agent projects fail because they skip scoping and jump straight to building. Here is what the process looks like when it works.
How We Built an AI Agent That Scaled a Contact Centre From 50 to 80+ Agents
A custom AI call quality monitoring agent replaced manual QA for a high-volume contact centre operation, enabling the team to scale from 50 to 80+ agents in three months without adding QA headcount. Here is exactly how it was built, what it cost, and what we would do differently.
AI Agent for Procurement Automation — How It Works in Enterprise
An AI procurement agent automates purchase requisition workflows — from request submission through approval routing, budget enforcement, and PO generation. Here's how it works architecturally, what it costs, and what Madgeek built for Tejas Networks.
AI Agent Development Cost and Timeline — What Enterprise Projects Actually Cost in 2026
A production AI agent costs $40,000-$80,000 to build and takes 8-16 weeks. This resource breaks down cost by complexity tier, timeline by phase, and the three variables that determine where your project lands in that range.
Why Enterprise AI Projects Fail at the Pilot Stage
Most enterprise AI projects fail not because the AI doesn't work, but because of three engineering failures that happen before the AI ever runs: broken data access, no failure handling, and no production monitoring.
AI Marketing Automation: Where HubSpot Stops and Custom AI Starts (2026)
Standard marketing automation platforms handle sequences, scoring, and basic personalisation. Custom AI is needed when scoring requires external data, personalisation logic exceeds the rule builder, or triggers depend on internal system events.
AI for Supply Chain: Why Most Implementations Fail and What Actually Works (2026)
Most supply chain AI implementations fail because the data isn't clean, ERP integration is scoped incorrectly, or the use case is too broad. This resource covers the three failure modes, what works in production, and how to scope a supply chain AI project.
AI for Customer Service: When Platform Tools Break and Custom AI Takes Over (2026)
AI for customer service reaches its ceiling when escalation logic is too complex, compliance prevents a SaaS vendor from handling the data, or the support workflow is deeply integrated with an internal system. This resource covers where the line sits.
AI for Finance: Where It Delivers ROI and Where It Doesn't (2026)
AI in finance delivers measurable ROI in automated reconciliation, anomaly detection, and procurement approval automation. This resource breaks down what works, what SaaS tools already cover, and when custom AI is worth the build cost.
How to Hire an AI Agent Development Company
A vendor selection guide for enterprise buyers hiring an AI agent development company. Covers the difference between chatbots and true agents, 5 capabilities that separate production builders from demo shops, architecture evaluation, the Agent Design Sprint model, and a 10-question pre-signing checklist.
Enterprise AI Integration — When and How to Add AI to Existing Systems
A production-focused guide to integrating AI with existing enterprise systems. Covers the three integration patterns, data readiness assessment, architecture for SAP/Salesforce/custom ERPs, realistic costs, common failure modes, and what successful integration looks like in practice.
How to Evaluate an AI Development Company
A structured vendor evaluation framework for CTOs and VPs Engineering hiring an AI development company. Covers the 7 critical questions, red flags that indicate demo-only experience, architecture questions for technical buyers, and how to run a paid evaluation sprint before committing to a full build.
AI Agents for Business: What They Can Automate and What They Cannot (2026)
AI agents can automate multi-step business processes that previously required human judgment — including document processing, lead qualification, procurement approvals, quality monitoring, and customer service escalation.
What Is Agentic RAG? How It Works and When to Use It (2026)
Agentic RAG combines an AI agent's ability to plan and act with dynamic retrieval of relevant information — enabling agents to answer questions accurately from large, changing knowledge bases that a static retrieval system cannot handle.
AI Agent Framework Comparison: Claude SDK, OpenAI Agents, LangGraph (2026)
The main AI agent frameworks in 2026 are Claude Agents SDK, OpenAI Agents SDK, and LangGraph — each with different strengths for tool use complexity, multi-agent orchestration, and deployment model.
AI for Manufacturing: What Production Systems Actually Look Like in 2026
AI in manufacturing is most commonly deployed for three problems: cost estimation, procurement approval automation, and quality control monitoring. This covers what those systems actually look like when built — not what vendors pitch.
What Is an Agentic Workflow? How It Works and When to Use It (2026)
An agentic workflow is a business process where an AI agent executes multiple steps autonomously — retrieving data, making decisions, taking actions, and escalating to humans only when needed.
AI Sales Automation: What Custom AI Does vs What Your CRM Already Does
AI sales automation handles the data-intensive parts of selling — lead qualification against ICP criteria, pipeline scoring, CRM enrichment, and deal prioritisation — but it does not replace the judgment and relationship work that closes enterprise deals.
Enterprise AI Use Cases: What Large Organisations Are Actually Building in 2026
The most common enterprise AI use cases in 2026 are quality monitoring, procurement automation, cost estimation, and sales pipeline management — all running on custom-built agents integrated with existing enterprise systems, not SaaS platforms.
What Is Agentic AI? Plain-English Definition With Business Examples (2026)
Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — unlike chatbots that only respond to prompts. This guide explains what that means for businesses with real production examples.