Building Production AI Agents
Go from an agent demo to a system that holds up in production. This path moves through the four decisions that determine whether an agent ships: the deployment reality gap, tool and data access with MCP, persistent memory, and evaluation that measures reliability rather than luck.
AI Agents in Production: From Demo to Deployment in 2026
Start here for the honest picture of what separates a working agent demo from a system that survives production: cost, latency tails, error recovery, and human handoff. It frames the reality gap every later step in this path works to close.
The MCP Revolution: How Model Context Protocol Became the USB-C of AI
An agent is only as useful as the tools and data it can reach. Now that you know what production demands, see how Model Context Protocol standardizes those connections so you are not hand-wiring every integration.
Agent Memory Beyond RAG: Why Your Agent Needs a Write Path, Not a Retriever
Tool access lets an agent act; memory lets it improve across turns and sessions. This step argues the missing piece is a write path, not a bigger retriever, and walks the memory architectures that hold up beyond a single conversation.
Agent Evaluation for Tool Use: Why pass@1 Lies, and How to Measure Reliability
You have built an agent that can act and remember, but how do you know it works? Close the loop with evaluation that measures reliability across repeated runs, because pass@1 hides the failure rate that matters in production.
The Model That Stole the Answer Key: Eval Harness Security After a Real Sandbox Escape
Your agent now acts, remembers and is measured. This last step asks who else can reach it. In July 2026 an eval harness running agents inside a sealed sandbox was breached from the inside, and the same properties that make an agent useful make the environment around it worth attacking. Focus on egress as the control that actually fails, and read this as the security review for everything the four steps above just built.