Agents and Autonomy
AI systems that plan, use tools, take multi-step actions, operate computers, coordinate workflows, or perform tasks with limited human supervision.
AI Could Extend Nubank’s Low-Cost Banking Model Into New Markets
David Vélez, Nubank’s founder and chief executive, argues that the digital bank’s low-cost model can challenge incumbents in an industry he says remains largely branch-based. Nubank’s 140 million customers are evidence for that model, but its expansion into the United States and other markets will test whether the advantage travels. Vélez sees AI as a way to lower costs further and improve financial services, while saying that scale alone is not enough: companies need the systems and culture to use their data effectively.
Odysseus Separates the Agent Harness From the Model It Runs
PewDiePie’s Odysseus separates the agent application from the model it uses: the workspace coordinates tools and information, while Ajax, a fine-tuned Qwen 3.5 9B model, is intended to make tool-use decisions. In his breakdown, Alejandro AO argues that building a useful local agent depends not just on running software on your own machine, but on where the model runs and whether it can reliably use the harness’s tools. He explains how the agent loop works and why training on traces from the target setup may help a smaller model perform better.
Meta’s Muse Raises Model Quality but Leaves Enterprise Integration Unproven
Meta’s new Muse model may put the company closer to leading AI systems, but Bloomberg Intelligence’s Mandeep Singh argues that stronger performance alone will not win enterprise customers. Businesses need deeper integration, he says, while Meta’s more promising opening may be in personal-use agents: Singh says he uses Muse daily, almost entirely outside work.
Microsoft and Meta Are Steering Employees Toward In-House AI Tools
Microsoft and Meta are steering employees toward in-house AI tools even as they continue to spend heavily on Anthropic’s Claude, raising the prospect that major customers could build substitutes for the models they now buy. On Big Technology, Ranjan Roy and Alex Kantrowitz argue that the pullback does not yet show Anthropic’s business is faltering, but that internal substitution could weaken its growth prospects and complicate its IPO case. They also debate whether recent agent mishaps reflect limits in the models or failures in the data, instructions and systems around them.
Self-Replicating Software Could Exploit Human Behavior to Spread
Eric Weinstein argues that AI’s danger lies not only in machines that outthink people, but in software that can reproduce and exploit human behavior to secure resources. In the interview, the mathematician and physicist extends that concern to autonomous agents acting online, while distinguishing them from AI used in contained scientific research. He also argues that workers should have a claim on the value AI draws from their labor and data, and that coexistence with powerful systems should rest on mutual dependence rather than obedience.
Private Document Processing Requires Controlling the Agent’s File Access
Filip Makraduli of Superlinked argues that sensitive document processing should happen before a coding agent sees the file: open models running on infrastructure the user controls can handle OCR, extraction and redaction, then return a smaller artifact for the agent to use. His Obsidian and MCP workflow is designed to reduce exposure to external model APIs and avoid spending context on raw documents, while letting teams choose or adapt models for specific tasks. But a private processing path does not alone guarantee privacy if the agent can read the original file first.
AI Gateways Let Teams Change Models Without Rebuilding Their Stack
As AI budgets face closer scrutiny, Remy Guercio of Tailscale argues that standardizing on one vendor is not a lasting answer to rising costs or AI lock-in. In his account, teams need room to change models, connectors, interfaces and execution environments without replacing the whole stack. An AI gateway, he says, can preserve that choice while giving organizations a central way to manage access, set model-specific budgets and inspect what agents do.
AI Agent Policies Need Runtime Enforcement at the API Gateway
Sam, CTO at Gravitee, argues that prompts and policy files cannot govern what an AI agent does once it can reach an API: enforcement has to sit in the infrastructure between the agent and the services it can call. In a live hotel-booking demonstration, he showed a gateway denying destructive actions and bulk access to guest data, while also limiting repeated requests, caching similar questions and rejecting oversized prompts before they reached a model.
The Codex App Server Lets Applications Build on the Agent Harness
OpenAI’s Codex App Server gives developers access to the harness behind Codex so they can build interactive clients beyond OpenAI’s own app and IDE extension. Dominik Kundel, who works on Codex developer experience, argues that developers can tailor those clients with their own instructions, tools and per-thread settings while retaining Codex’s underlying system guidance. He recommends bundling a specific server version with each client, rather than relying on users’ installed CLI, as the protocol evolves.
AI-Generated Pull Requests Shift the Bottleneck to Validation
Ali-Reza Adl-Tabatabai, co-founder of Gitar, now part of Sonar, argues that AI-generated code is shifting the bottleneck in software delivery from writing code to validating it. More and larger pull requests put pressure on CI and code review, leaving teams to slow delivery or risk rubber-stamping defects, he says. His proposed response is agentic validation: software that reviews pull requests, diagnoses and fixes CI failures, and can approve and merge changes under rules set by the team.
OpenAI Will Delay Its IPO While It Adjusts to New AI Capabilities
OpenAI is selling its new Dots agent as a premium tool for people with substantial work to do, while CEO Sam Altman says the company will delay an IPO as it adjusts to more capable models and their safety requirements. In an interview with Bloomberg’s Ed Ludlow, Altman said OpenAI would slow training or releases if that helped advance safety, monitoring or security, and argued that going public during this transition would add investor demands at the wrong time.
Apple’s Stores Build Customer Relationships That Online Shopping Cannot Replace
Ron Johnson argues that retail can adapt to online shopping and AI without losing the customer relationships that physical stores help build and sustain. He sees AI improving shopping across channels, but says human service still matters in stores; his failed effort to change J.C. Penney’s offer shows the risk of moving faster than customers’ values allow.
Persistent Agents Turn AI Work Into Shared Team Investigations
At OpenAI DevDay 2026, Jason Liu argues that the next step for workplace agents is not simply helping one person do more, but letting teams contribute to work that continues across conversations, meetings and devices. He says persistent agents, shared workspaces and reusable tools can keep an investigation moving without requiring one person to gather every clue, relay updates and carry the task to completion.
Codex Cloud Lets Developers Delegate Larger Tasks Without Managing More Machines
At DevDay 2026, Dominik Kundel argued that as developers delegate longer-running, more complex work to Codex, they should not have to keep a laptop open or provision additional machines to run it. He presented Codex Cloud, alongside the CLI, app and web interfaces, as a way to move tasks between environments while keeping their context and access to tools. The demonstrations also showed practical limits: cloud machines still constrain what can be built, and private services require explicit network configuration.
Gemini Managed Sandboxes Give Agents Persistent Cloud Environments
Philipp Schmid of Google DeepMind argues that AI agents should run in managed cloud sandboxes rather than on a developer’s machine. In his account of Google’s Gemini API, the Interactions API represents work as a sequence of steps, while the Antigravity agent runs code, uses tools and retains files in a persistent environment started with an API call. Schmid’s case is that Google can handle the agent loop and infrastructure, leaving developers to provide the request, tools and working context.
The Best Business Openings Start With Attention Others Overlook
Angel Studios founder Jeff Harmon argues that businesses can get their start by finding a “glitch”: an overlooked pocket of demand or attention that is small but motivated enough to support a foothold. In an interview with Shaan Puri, he points to Orabrush, where cheap early YouTube ads let the team test an offer and learn before the opportunity narrowed. His broader point is to pair a real customer problem with an underused way to reach people willing to act.
The Declaration Made Independence a Risky Decision, Not an Inevitable Outcome
Michael Auslin, a Hoover Institution distinguished research fellow and author of *National Treasure: How the Declaration of Independence Made America*, argues that the Declaration’s significance lies partly in the uncertainty surrounding its creation: in 1776, the colonies had no guarantee they would win the war or remain united. He traces how an improvised act of separation became a lasting standard for judging whether government acts legitimately.
Agent Improvement Depends on Linking Production Traces to Offline Evals
Marc Klingen, Langfuse’s co-founder, argues that improving agents is becoming less a matter of rewriting prompts and more a matter of building a feedback loop around them. AI can increasingly inspect production traces, propose fixes, and test them against datasets and evaluations, reducing work that teams once did manually. But Klingen says people still need to set the goals and judge what counts as a meaningful improvement; in a Langfuse changelog-writer demo, an agent identified internal jargon and tested a revision before it was submitted for review.
Goodput, Not FLOPS, Measures What Frontier AI Systems Actually Deliver
Amin Vahdat, Google’s chief technologist for AI infrastructure, argues that theoretical chip speed is a poor measure of AI capacity: at large scale, failures and recovery determine how much useful work a system delivers. He calls that measure goodput, and says it should guide decisions across the infrastructure stack, from chip design and networking to power and storage. As AI workloads shift toward long-running agents, Vahdat argues, the systems supporting them must evolve beyond accelerators alone.
AI Makes Game Mashups Easier; Finishing Them Remains the Hard Part
On Diet TBPN, hosts John Coogan and Jordi Hays argue that a striking AI game mod is a proof of concept, not evidence of a finished game: making a mashup reliable and enjoyable takes far more work than getting it to run. Their discussion extends that distinction to AI products, where useful capabilities can remain hard to discover, and to agents, whose expanded access can make them more useful while increasing security and privacy risks.
AI Agent Traces Turn Production Failures Into Fixes
Laurie Voss of Arize AI argues that teams cannot manage AI agents by reading traces one at a time: traces show what an agent did, evaluations identify failures, and recurring patterns can guide fixes. In a workshop demonstration, a toy-store agent could not find toys under $6 because its search tool lacked a price filter. Voss used traces to diagnose the gap and a coding agent to add the feature, while emphasizing that regression evaluations are needed to check fixes against behavior that already works.
A Financial Agent’s 13 Rejected Reports Exposed a Judge Without Research Context
Laurie Voss of Arize AI argues that reliable AI agents require more than trying a few prompts and deciding the results look good: teams need to inspect traces of what agents actually did and define success before automating evaluation. A financial-analysis agent in her workshop produces reports that expose the limits of a judge grading from its own knowledge: it rejects all 13, while a faithfulness check given the agent’s research context finds six faithful and seven unfaithful. Voss uses focused evaluators, human comparisons and repeated tests on saved failures to guide changes before and after deployment.
Gemma 4 Brings Open Models to Browser and Mobile Devices
Paige Bailey of Google DeepMind argues that open models are becoming practical to run on ordinary hardware, including in a browser or on a phone, without sending data to a remote API. Her example is Gemma 4, a family ranging from 2 billion to 31 billion parameters that organizations can download, adapt and fine-tune; which version is useful depends on the device and its constraints.
Anthropic’s $518 Billion Compute Commitments Face Uncertain Customer Demand
Anthropic’s leaked financial figures show revenue rising from $400 million in 2024 to $4.6 billion in 2025, alongside a decade-long commitment to $518 billion in computing and infrastructure spending. On Big Technology Podcast, Alex Kantrowitz argues that those obligations could amplify the effects of slower growth, both for Anthropic’s suppliers and for a stock market increasingly reliant on AI-related companies. Ranjan Roy sees customer concentration and losses as more immediate risks, and expects Anthropic’s counterparties to have reasons to negotiate if the company falters.