From the AIFlow team
Product updates, guides, and field notes on building with autonomous AI agents.
Field notes: when the agent costs more than the developer
A single agentic task fires off 5 to 30 model calls. Without per-task tracking, cost becomes a number you only discover on the invoice.
Choosing the Right Model for Every Workflow Step: Accuracy Without Overspending
Using the most powerful model everywhere feels safe. It is not. Here is how to match model capability to task complexity, cut costs significantly, and often improve accuracy at the same time.
A Field Guide to Open-Source Python Agent Harnesses
Eight open-source Python harnesses for building AI agents, from deepagents and LangGraph to the Claude Agent SDK. What each one is good at, where it struggles, and how to choose between them.
Multi-Agent Systems Patterns: A Practical Guide to Designing Agent Workflows
One agent is rarely enough for real enterprise work. The question becomes how to coordinate many of them. Here are the multi-agent patterns that hold up in production, and how to pick between them.
Shadow AI: The Hidden Risk Lurking in Your Organization
When employees adopt AI tools without IT oversight, they open the door to data leaks, compliance violations, and security blind spots. Here is what Shadow AI looks like and how to get ahead of it.
Welcome to the AIFlow blog
Product updates, engineering notes, and stories from the field as we build the enterprise AI agent platform.