AI agents need reliability built into their architecture from day one to avoid the ongoing "reliability tax" of operational breakdowns, legal exposure, and reputational damage from unreliable autonomous systems.
New research gives AI agents procedural memory that learns from failures and transfers between tasks. Early results show higher success rates with lower token costs—potentially solving the economics that have held back agent adoption.
On the In-House podcast, I shared why AI won’t erase lawyers — but it will change every role inside the legal function. Tools may act like lawyers, yet judgment and oversight remain squarely human.
Block and GSK show successful AI agents adapt to existing workflows rather than forcing teams to rebuild around technology. The key is making AI feel invisible while amplifying human expertise.
RAG isn't broken—it's that we treated it as the default when it should have been the exception.
Your data architecture is your governance architecture—build accordingly.
Companies learned that chatbots you can bolt onto existing systems are fundamentally different from AI agents that orchestrate workflows across CRM, supply chain, and finance operations.
Companies that succeed with AI agents aren't just automating tasks—they're rebuilding how work gets done. The difference between adding agents to old workflows versus designing new ones around agent capabilities.
