How to make your business agent-ready
Eight steps that let AI agents find, buy from and get help from your business as customers, with a scorecard, disqualifiers and a checklist for this quarter.
Architecture
Reference designs, orchestration, tool use, memory and evaluation for agents that run in production.
The framework behind this pillar: Agentic in Production.
Single agent vs multi-agent AI: how CTOs should choose
Single agent or multi-agent? Atallah says ten specialists beat one superagent. Masad runs one agent across Replit. Here's how CTOs should choose.
Agent harness playbook: let the model choose, not act
How to add a decision layer to an agent harness without handing the model authority. Eight steps for a CoE or R&D team, the risks, the compliance checks, a lab.
MCP vs API: why the third AI agent costs more than the first
MCP vs API in plain English for department heads and CXOs. One remote per device against one USB-C port, and the sum that decides what your agents cost.
What 306 agents in production taught me about enterprise AI
Leverge runs 306 agents across 13 functions. Where they cluster, why the process is the unit rather than the agent, and the rule that makes enterprises say yes.
Why most enterprise AI agents never leave the demo
Enterprise AI agents fail between demo and production for organisational reasons, not model reasons. The three gaps they die in, and what closes each one.
Agentic AI reference architecture for enterprise
A six-layer reference architecture for enterprise AI agents - work definition, orchestration, tools, memory, guardrails, operations - with steps and checklists.