Topic
Agents
Delegating whole jobs, and what it takes to trust the result.
Agents at the bank's door
In October, the largest US banks shut out the AI agents their customers had started using to manage money. What they blocked, why it is hard, and a practical roadmap for financial institutions to let agents in safely.
Early October 2026: AI gets a budget
The month in AI, first half of October. What business leaders should take from two weeks of AI news.
The model is eating the harness
Work that AI systems once had to build around the model keeps becoming part of the model. What that pattern looks like, why it keeps happening, and how to build systems that get better with every release.
The AI-native finance team
What a finance department looks like when agents do the routine work: which workflows they handle today, how they connect to the ledger, how the team changes shape, and how to start.
How to measure applied AI
A model's benchmark score says little about how it will do on your work. A practical playbook for measuring AI inside an enterprise: real cases, outcome metrics, repeated runs, one scorecard and a loop that keeps measuring after launch.
The Bending Spoons playbook
Bending Spoons runs more than 50 acquired software businesses on one shared platform with a core team of about 600. What the platform contains, why AI changed its economics, and how any roll-up or private equity firm can build its own.
One view of the business
Private equity owners and management teams often see the same company differently. A playbook for the systems that close the gap, with less reporting work for both sides.
September 2026: the month agents moved in
The month in AI. Always-on agents went mainstream, frontier models got cheaper and harder to get at the same time, and safety stopped being theoretical.
The AI campaign playbook
In 2026 campaigns used AI to draft. In 2028 the first campaigns will run on it. A department-by-department guide to the operation behind a modern campaign.
The risk isn't the deepfake
Deepfakes get the headlines. The larger risk for a campaign is the AI it deploys itself: answering voters, speaking in the candidate's name, at a scale no one reviews.
The Long Lake playbook
A three-year-old AI holding company just closed a $6.3 billion take-private of Amex GBT. How the model works, what it claims, what is still unproven, and what private equity should take from it.
Building an AI roll-up platform
Private equity invented buy-and-build. The AI roll-up is being built mostly outside it. How a private equity firm can run the model, from choosing the vertical to the platform, the people and the exit.
The credit manager's AI playbook
Where AI earns its place across a credit manager, from origination and monitoring to CLO operations and the wealth channel, and what has to be true first.
The private equity AI playbook
What a fund should actually do with AI at each stage of an investment, from diligence through exit, and how to build AI EBITDA that survives the next buyer's diligence.
Beyond cost: AI as a revenue engine
Most portfolio AI plans are cost plans. The larger and more durable opportunity is revenue: capacity that turns into growth, new AI products and services, and new ways to price.
AI across a portfolio, sector by sector
The same technology lands differently in software, services, healthcare, defense, industrials and home services: different work, different constraints, and a different share of the gain the company keeps.
The deskless majority
Most of the workforce has never had a keyboard, a login, or an hour to spare for training. The interface problem that kept them off enterprise software is the one this generation of AI removes.
Where AI helps across a university
A guide to the work AI genuinely helps with today, area by area, from admissions and advising through research administration and accreditation, and what has to be true for it to help.
Talking is not solving
Voice platforms advertise containment. Containment measures whether a call ended without a human. It says nothing about whether the caller's problem went away.
Where voice actually works
Voice stopped being a demo this year. A map of what it is good at now, what it is still bad at, and the places it should not go.
What an ontology is for
The word has escaped into every enterprise AI pitch. Here is what it actually means, why agents made it urgent, and how it becomes the foundation of an AI transformation.
Agents need your decision model, not just your data
Retrieval gives an agent what the company knows. A decision model gives it how the company decides, and that is what lets it act.
Meet the work where it happens
Fig is not religious about form factor. Slack or Teams, the mobile app or WhatsApp, desktop or web: the AI fits how your organization operates.
Closing the books with agents
Where agents take work off the close calendar, where people stay in the loop, and what the audit trail has to capture.
Getting the CISO to yes
The five questions security will ask about an AI agent, the four proofs that answer them, and a rollout security can approve.
Introducing Voice Mode on Fig
Everything you do with Fig in chat, you can now do with your voice. How voice mode works, where it runs, and what it is for.
Mastering Your Workflow with Fig Scheduled Tasks
Scheduled tasks let Fig run recurring work on its own: daily briefs, weekly reports, and monitoring that shows up before you ask.