Notes and updates
The Tandem blog
Stories, guides, and updates from the team.
The More Interesting Agent Loop Is Repair, Not Coding
How autonomous coding agents become reliable — a runtime that observes failures, triages root cause, verifies fixes, and remembers what worked.
Agents Authoring Agents: What We Learned from Wiring Tandem's Docs to an LLM
We wired Tandem's MCP-accessible docs to an LLM and got a schema-correct mission blueprint on the first try. This article explains why documentation, provenance, and ownership matter for agents authoring agents.
The Agent Boom Is Solving the Wrong Problem
Everyone is racing to make AI agents easier to install. That is not the hard part anymore. The hard part is turning vague human intent into work a team can actually trust.
From Human Intent to Agent-Assisted Workflow Authoring
Most AI tools can produce an impressive demo. Far fewer can turn messy real-world intent into workflows that people can trust, inspect, revise, schedule, and run again. Here is how Tandem is solving that.
Why Tandem Is Built for Reliable Agent Execution
Most agent systems fail not because the model is weak, but because execution breaks down under compounding uncertainty. Tandem is built to solve that problem—helping teams ship agentic workflows that are trustworthy, observable, and economically bounded in production.
Workflow-First Autonomy: Why Reliability Beats Demo-Only Agents
Most AI agents impress in demos and collapse in production. This post explains why workflow-first autonomy—built around durable state, explicit checkpoints, and safe recovery—is the only architecture that holds up under real conditions.
Reliable Agentic Workflows Need More Than Demos
Why agentic systems fail in production—and what it actually takes to run multi-step workflows reliably. A practical look at state management, failure recovery, and workflow control, and how Tandem is built to address each.
Workflow-First Autonomy: Why the Market Is Rejecting Opaque Agents, Not Agentic Automation
Teams aren't cooling on automation—they're rejecting black-box AI agents that can't be inspected, controlled, or reliably recovered. Here's what workflow-first autonomy looks like in production, and why Tandem is built for governed, observable execution.
Tandem Turns Agentic Systems Into Operable Software
Agentic systems fail when runtimes are brittle, workflows are hard to author, and governance is expensive. Here is how Tandem's engine-backed approach makes AI autonomy dependable enough for real production operations.