AI Agents Hour — Mar 2026 till Feb 2026
Watch and listen to past episodes, breaking AI news, guests from the industry, and technical deep dives on building AI agents.
March 24, 2026
#74
Email Broke Productivity - It's Time To Fix It (with Brett and Naveen from Micro)
Brett Goldstein and Naveen Sreekandan from Micro join Shane and Abhi to talk about why they believe the future of productivity looks completely different from what we have today. Micro is an all-in-one productivity platform: email client, CRM, calendar, tasks, docs, meeting notes, and a powerful AI agent, all built on a unified graph where every object (like emails, people, companies, meetings, documents) is interconnected. The thesis is simple but bold: email isn't just a list of messages to get through. It's the world's most-used CRM, travel app, hiring tool, and developer notification system. Micro restructures that data so each use case actually feels like the right tool for the job — your sales pipeline as a Kanban board, your GitHub notifications as a task board, your contacts fully enriched from every email and meeting you've ever had. Brett walks us through the demo: the daily orchestrator automation that audits itself, updates its own prompt, generates your day plan, and has even prepped talking points for this interview. Context docs let the agent know everything it needs. The CRM auto-fills and auto-updates from emails and meeting notes. The X integration lets the agent pull recent posts from anyone you're about to meet. Naveen covers the architecture: built on Mastra, using agent and workflow primitives on top of a graph-based data model backed by Postgres with a custom query layer called Prism. One main agent with dynamic context injection handles both chat and automations — the agent knows whether it's in automation mode (just give the output) or chat mode (ask follow-up questions). Supermemory powers vector search. Dedicated sub-agents handle specific workflows, such as email labeling and meeting note summarization.
March 20, 2026
#73
Two Lines of Code to Lock Down Your Agents - Mastra Studio Auth
Mastra Studio started as a local playground for developers to test agents and workflows without having to spin up a custom UI. But as the feature set grew, teams started asking: how do we share this with non-technical teammates? How do we control what different users can do? Ryan, an engineer at Mastra, walks through the new Mastra Studio Auth — now baked directly into Studio. Starting with simple token-based auth (two lines of config), you can lock down your Studio from the open internet. From there, RBAC lets you map roles to granular permissions — 80 auto-generated permissions derived directly from Studio's routes and handlers, controllable via wildcard patterns. Out-of-the-box providers include WorkOS, Auth0, Supabase, Firebase, and Clerk, with GitHub and others in open PRs. The team also discusses what's coming next: audit logs so you can see exactly what an agent did, why it accessed a given tool, and whether it should have. Auth for agents in production isn't magic — your tool files still need to check permissions — but Mastra handles the plumbing so you can focus on building securely.
March 18, 2026
#72
NVIDIA GTC, The Death of MPC, and AI Agents Are Hiring Humans - This Week in AI
Shane hosts this week's news from his usual studio while Abhi joins remotely from NVIDIA GTC 2026 in San Jose. Jensen Huang's keynote set the tone: NVIDIA is doubling down on AI factories, pushing 100x more token throughput, and helping bring OpenAI onto AWS infrastructure.
March 12, 2026
#71
Meta Acquires Moltbook, Openai Releases GPT 5.4, TypeScript Is #1 on GitHub (This Week In AI)
A lot happened in eight days. Meta acquired Moltbook, a social network built entirely for AI agents, not humans. OpenAI dropped GPT-5.4 Thinking and GPT-5.4 Pro, Codex got forks for multi-agent workflows and Windows support, and there are rumblings of OpenAI building a GitHub alternative. Anthropic fired back hard — multi-agent PR code review for Claude Code, while loops via /loop, the Claude Marketplace, and a way to pull your context from other AI tools.
March 10, 2026
#70
The Biggest Threat to AI Agents (with Ismail Pelaseyed)
Ismail Pelaseyed from Superagent is back on Agents Hour, and this time he's talking about something most builders aren't thinking about yet — supply chain attacks on AI agents. Guardrails protect against what you tell your agent to do. But what about everything your agent reads, fetches, and installs on its own? That's the gap Brin is built to fill.
March 4, 2026
#69
Missile Strikes Disrupt AWS and Claude, Anthropic Banned from US Government, Cloudflare vs Vercel
This week in AI saw geopolitical turmoil, major funding news, and a shift in software development. Missile strikes in the UAE and Bahrain disrupted AWS and Claude services. Meanwhile, after Anthropic banned its models from autonomous weapons and mass surveillance, the Trump administration banned Anthropic from government contracts—posing a major supply chain risk. On the same day, Sam Altman secured a deal with the Department of War as OpenAI announced a $110 billion funding round, highlighting a sharp contrast in approaches.
March 1, 2026
#68
How to Build Reliable AI Agents with Datasets, Experiments, and Error Analysis
Yujohn from Mastra explains why datasets and experiments are essential for building production-grade AI agents. If you're building an agent, you need a way to verify it's working correctly before and after you make changes. Datasets provide that baseline. You create a collection of test cases (ground truth) that represent the scenarios your agent should handle. Then you run experiments: pass each test case through your agent and measure the results. This is error analysis in practice. You start by identifying where your agent fails, then build scorers to quantify those failure modes over time. Smaller teams often ship first and add datasets later, once they have user feedback. Larger teams need them earlier. But eventually, every production agent needs this. The demo shows how Mastra makes this accessible. You can create datasets through the UI, add items manually or import from CSV, and run experiments with a single click. The results show you exactly what went wrong: which tool calls failed, what the agent output was, and how it compared to ground truth. You can also compare experiments side by side to see if your prompt tweaks actually improved things. And because all the data lives in your own database, you can write your own agents to analyze the results, dig into traces, and iterate. The SDK makes it easy to integrate into CI/CD: run experiments on pull requests, gate deployments on eval scores, or just collect data from production and curate datasets later.
February 27, 2026
#67
A Coding Agent That Never Compacts
Abhi walks through Mastra Code, a new open-source coding agent with observational memory that compresses context without losing it.
February 25, 2026
#66
AI NEWS: Stripe's Minions, Distillation Attacks on Claude, Cloudflare's Code Mode
Shane and Abhi break down the biggest AI news from the past few days. Anthropic identified industrial-scale distillation attacks on Claude by DeepSeek, Moonshot AI, and MiniMax. Anthropic also released a groundbreaking report analyzing millions of AI agent interactions using Claude. Stripe is shipping 1,300+ AI-generated PRs per week with their Minions system. Code Mode for MCP is becoming a standard part of the MCP ecosystem, and we cover skills benchmarks, trajectory explorer for agent traces, Vercel AI Gateway video support, and more.
February 24, 2026
#82
Sazabi: AI-Native Observability for Fast-Moving Teams (with Sherwood Callaway)
In this episode, Shane and Abhi sit down with Sherwood Callaway, founder of Sazabi, an AI-native observability platform designed for engineering teams that move fast. Sherwood shares his journey from building infrastructure and observability teams at Brex to realizing that modern development tools are moving at light speed, while observability tooling hasn't kept pace. While AI agents can ship thousands of lines of code per day, teams are still debugging production with the same tools they've been using for years: Datadog, Sentry, manual dashboards, and manual incident triage. Sazabi takes a radically different approach to observability centered on three core principles: 1. Less is More — Debugging an incident is as simple as asking a question. "Why is production down?" The best UI for observability is chat. 2. Logs Are All You Need — The "three pillars of observability" (logs, metrics, traces) is outdated dogma. With AI, you can accomplish everything using just logs. Logs are events, metrics are aggregated events, and traces are collections of start/end events. Logs can do it all. 3. Monitoring as We Know It is Dead — Sazabi replaces static monitors with agentic anomaly detection. Think of it as a team of staff engineers constantly watching your app for issues, investigating problems, and only escalating what matters. In this conversation, we dive into the gap between modern development and modern observability, and why the idea that “logs are all you need” is both controversial and, in Sherwood's view, correct. We also explore how Sazabi uses AI agents for root cause analysis (RCA), the philosophy behind simplifying observability for all engineers, and the company’s current status.
February 24, 2026
#65
How to Orchestrate Coding Agents with Conductor, with Charlie Holtz
Shane and Abhi welcome Charlie Holtz from Conductor to AI Agents Hour. Charlie shares how frustration with managing multiple Claude Code instances led to building Conductor. They discuss Conductor's July 2025 launch as the first agent orchestration Mac app, early design choices, and its impact on the market.
February 20, 2026
#64
AI NEWS - Something Big is Happening: Gemini 3.1 Pro, GPT-5.3-Spark, and Anthropic $30B fundraise
It's time for another AI News roundup with Shane and Abhi! This week was absolutely massive. Matt Shumer's viral article about AI automation, which describes his own job being automated in real time, has reached 84 million views. Anthropic raised $30 billion at a $380B valuation (one of the largest private raises in tech history). Claude Sonnet 4.6 launched with a 1M token context window. And the Chinese model tsunami is real: Qwen 3.5, GLM 5.0, MiniMax M2.5 (nearly Opus-level at 1/8 the cost), and DeepSeek v4 rumors.