Agents and Autonomy
AI systems that plan, use tools, take multi-step actions, operate computers, coordinate workflows, or perform tasks with limited human supervision.
Retrofitting Sovereign AI Turns Compliance Rules Into Architecture Rework
Bilge Yücel of deepset argues that AI sovereignty is an engineering constraint that has to be designed into a system, not a legal or procurement requirement applied after deployment. She frames sovereign AI around control of data, models, infrastructure, and operations, and shows how retrofits expose hidden dependencies: jurisdiction-crossing data flows, model APIs embedded in application logic, managed services that masked operational work, and systems that cannot be traced or audited.
Every Addition to an AI Agent Can Make It Worse
Ara Khan of Cline argues that agent maturity is less about adding autonomy than about knowing what not to add. In a talk structured around four levels of agent building — from frameworks to state machines, Kanban-managed workflows and cloud deployment — Khan says frontier models increasingly reward simpler prompts, deliberate architecture and visible human control. His central warning is that every extra instruction, abstraction or automation layer can make an agent worse.
Serval Bets Boring IT Controls Will Unlock Enterprise AI
Serval founder and CEO Jake Stauch argues that enterprise AI will be won less by giving models broad autonomy than by constraining them inside permissions, approvals, audits and workflows that companies can trust. In a conversation hosted by Sequoia’s Pat Grady, Stauch describes Serval as a ServiceNow-like system rebuilt for AI: an admin agent generates workflows from natural language, while a help desk agent can act only through tools IT has explicitly approved. He says that same logic extends to Serval’s operating model, where customer insight and “fewer, better” hiring matter more than model access in a market that may force products to be rebuilt every few months.
AI Growth Is Running Into Power, Memory, and Inference Bottlenecks
TBPN’s discussion recast the AI boom around physical and economic bottlenecks — power, cooling, chip scarcity, inference cost and memory — rather than model ambition alone. Mike Isaac, Rowan Trollope and Dean Leitersdorf described an industry where local utilities, low-level inference optimization and fast state management are becoming central constraints, a capacity problem the hosts also saw in the whey protein shortage. Everlane’s reported sale to Shein pointed to a different limit: Hays argued that venture-backed ethical basics struggled against price pressure, brand preference and the demand for sustained growth. Joanna Stern supplied the adoption constraint, arguing from her reporting that AI’s progress will be judged through trust, job anxiety, children’s safety and whether new devices ease or deepen phone dependence.
Recursive Emerges From Stealth at $4.65 Billion Valuation
Recursive CEO Richard Socher told Bloomberg that the newly disclosed startup is trying to build AI systems that can automate the research loop: proposing ideas, implementing them, testing them, and using the results to improve AI itself. The company emerged from stealth with more than $650 million raised, a $4.65 billion valuation, and backers including GV, Greycroft, Nvidia, and AMD. Socher argued Recursive’s edge is an organization built around open-ended AI experimentation, while Bloomberg’s Caroline Hyde pressed him on compute costs, safety, hiring, and why the work belongs in a separate lab.
ServiceNow Says Agentic AI Lifted HR Capacity and Automated Support Work
ServiceNow executives Jacqui Canney and Kellie Romack argue that agentic AI is already changing workplace operations by creating measurable capacity rather than simply replacing jobs. In a ServiceNow-sponsored interview, they point to the company’s internal deployments — including faster commission answers, autonomous IT service-desk resolution, and large-scale support automation — as evidence that AI’s value depends on redesigning workflows, tracking the capacity created, and redeploying employees into higher-value work. Their case is that managers now have to govern both people and agents, with visibility, skills assessment, and explicit choices about what work should be automated.
Long-Running Agents Need Separate Builders, Evaluators, and Disposable Scaffolding
Anthropic’s Ash Prabaker and Andrew Wilson argue that long-running agents are a harness-design problem, not a matter of writing longer prompts. Their case is that agents can run for hours only when building, judging, planning and state management are separated: adversarial evaluators should test live behavior, work should be decomposed into explicit contracts, and durable state should live outside the model’s context. They also warn that this scaffolding is provisional, because each new model release changes which supports are useful and which have become dead weight.
Drones and Sensor Networks Are Turning Policing Into Real-Time Response
David Ulevitch’s a16z conversation with Arizona DPS director Jeffrey Glover and Flock Safety’s Rahul Sidhu argues that public safety technology is moving from record-keeping and faster response toward earlier situational awareness. Sidhu describes drones, license-plate readers and gunshot detection as a layered system for proactive response, while Glover says agencies are building broader technology ecosystems that also monitor officer wellness, analyze body-camera footage and share intelligence across jurisdictions. Both argue that founders need direct exposure to field work if they want to build tools that departments can actually use.
Cheap Autonomous Drones Are Rewriting the Economics of Land War
Yaroslav Azhnyuk, the Ukrainian tech founder behind The Fourth Law, argues in a long interview with Noah Smith and Brandon Anderson that Ukraine has already revealed a new form of war built around cheap, mass-produced, increasingly autonomous drones. FPV drones, he says, have displaced artillery as the main killer on the front, while China’s manufacturing capacity and Western procurement habits point to a widening strategic gap. His case is not that tanks, artillery, infantry or aircraft have disappeared, but that militaries planning around scarce, expensive platforms are misreading the economics of the modern battlefield.
A Harness Made GPT-3.5 Turbo’s Browser Agent Reliable Without Rewriting the Prompt
Tejas Kumar, an IBM engineer, argues that unreliable AI agents are often not suffering from bad prompts so much as missing harnesses: the deterministic software around a model that bounds its behavior, manages context, verifies outcomes, and handles known failure states. In his Hacker News browser-agent demo, GPT-3.5 Turbo falsely claimed it had upvoted a post after hitting a login wall; without changing the prompt, Kumar added guardrails, trace-based verification, and a programmatic login handler until the same model completed the task reliably.
Incident.io Uses Coding Agents to Debug Its AI SRE
Lawrence Jones, founding engineer at Incident.io, argues that complex AI products now require debugging tools built for agents as well as humans. In a talk on Incident.io’s AI SRE system, which runs hundreds of prompts across telemetry and code during production investigations, Jones describes how the team moved from human trace inspection to agent-addressable evals, downloadable file-system traces, and parallel analysis pipelines to find and fix failures that had become too large to debug manually.
AI Chat Needs Shared Sessions, Not Single Response Streams
Mike Christensen of Ably argues that many AI chat interfaces fail because they tie the user experience to a single streaming connection, not because the underlying model is inadequate. In his account, Server-Sent Events make common product behaviors such as refresh, reconnect, cancellation, multi-tab use and device switching brittle or ambiguous. Christensen’s proposed fix is to treat the AI session as a durable shared resource: clients and agents subscribe to and write into the session, so connections can drop, agents can run concurrently, and humans can join without losing context.
Agentic AI Is Turning Model Quality Into a Systems Problem
At AI Engineer Singapore’s second day, speakers from Google DeepMind, Cloudflare, Arize, OpenClaw, Adaption and other teams made a shared engineering case: as AI systems become more agentic, model quality is no longer separable from the systems around the model. Richard Ngo framed the risk as long-horizon, situationally aware agents whose goals cannot be inspected, while practitioners argued that production AI now depends on continuous evaluation, traces, deterministic execution boundaries, routing, memory, fine-tuning and test-time search. The source’s central claim is that useful and safe agentic AI is becoming a systems problem, not just a model-selection problem.
Playwright Lets Agents Test Feature Requests Before They Write Code
Microsoft’s Marlene Mhangami argues that AI-generated tests can make a codebase look healthier than it is, because agents often write tests that confirm their own implementation rather than validate the user-visible behavior a feature is meant to deliver. Her prescription is to reverse the common workflow: start from the feature request, have the agent write failing Playwright tests against expected behavior, then generate code to pass them. In a GitHub Copilot demo using the Playwright MCP server, she applies that approach to a toy-store search and filtering feature, with the browser showing the agent exercise the product experience directly.
Context Graphs Make AI Decision Trails Queryable
Stephen Chin of Neo4j argues that enterprise AI systems need context graphs because retrieval alone can surface relevant facts while missing the relationships that make them usable. In his examples, a graph-augmented system can connect a patient’s emphysema care plan to smoking history or a credit decision to prior rejections, policies, margin trades and fraud signals. Chin’s case is that agents should preserve not only documents and answers, but the decision traces, tool calls, causal chains and outcomes that let humans inspect and reuse prior reasoning.
Economic Entanglement, Not Decoupling, Defines the New China Bargain
Salesforce CEO Marc Benioff joined the All-In hosts for a discussion that framed U.S.-China relations, enterprise AI, and the software selloff around the same question: when dependence is a stabilizer and when it becomes leverage. Benioff argued that more trade with China can lower conflict risk and that large software platforms remain valuable because AI still needs trusted customer data, cash-flowing distribution, and enterprise deployment. David Friedberg, Chamath Palihapitiya, and Jason Calacanis extended the argument across Taiwan, chips, AI assistants, El Niño-driven food risk, and private-market SPVs, where interconnection can either absorb shocks or transmit them.
AI Software Winners Will Own Context, APIs, or Outcomes
Tasklet chief executive Andrew Lee argues that AI software is consolidating toward a few horizontal agent platforms that hold context, connect tools, generate interfaces, and choose among models. In a discussion with Nathan Labenz, Lee says Tasklet has rewritten its agent stack around file-system memory, agentic search, and provider-specific context management because the chat transcript is no longer enough. He also frames Anthropic as both Tasklet’s critical supplier and a major competitor, making model neutrality central to Tasklet’s bid to survive the AI transition.
Figure Claims 50-Hour Autonomous Humanoid Test Was Not Teleoperated
Figure chief executive Brett Adcock told Bloomberg that the company’s livestreamed humanoid package-sorting test is fully autonomous and not remotely operated, rejecting viewer claims that repeated hand motions suggested teleoperation. Adcock said the robots were running on Figure’s onboard Helix 2 neural network, had operated for close to 50 hours with little downtime, and had pushed nearly 60,000 packages through the line. He framed the demonstration as evidence that Figure is moving toward commercially useful, human-speed humanoid robots built through a vertically integrated hardware, manufacturing, data and AI stack.
ChatGPT for Excel Adds Audit Trails to Finance Workbook Reviews
A demo of ChatGPT for Excel shows how finance teams could review a CFO performance workbook before it reaches leadership. The case it makes is constrained: ChatGPT inspects the model in Excel, flags tie-out breaks, stale source data and variance issues, applies only mechanical cleanup, and creates workbook tabs for the issue log, fixes, remaining risks and owner questions. The source presents the tool less as a substitute for financial judgment than as a way to put a documented audit trail and readiness verdict inside the file itself.
Self-Driving Startups Shift From Science Risk to OEM Deployment
Wayve chief executive Alex Kendall and Waabi chief executive Raquel Urtasun argue that self-driving has moved from a basic research problem to an execution problem built around end-to-end AI, world models, OEM partnerships and deployment economics. In this This Week in Startups discussion, Kendall makes the case for licensing Wayve’s “intelligence layer” across consumer vehicles and robotaxis, while Urtasun says Waabi’s L4-native Driver-as-a-Service model can scale first through trucking and then robotaxis. Both reject the idea that autonomy is simply solved, but they present the remaining challenge as integration, validation, regulation and commercialization rather than a missing scientific breakthrough.
Legacy Infrastructure Is Slowing Enterprise Agentic AI Adoption
Kris Lovejoy, global strategy leader at Kyndryl, argues that enterprises are not being held back from agentic AI mainly by model capability or startup speed, but by the difficulty of running agents securely and reliably inside legacy infrastructure. In a conversation with Craig Smith, she says pilots are widespread but scaled deployments remain rare because agents need context, governance, compliance controls and modernized IT foundations before they can touch core systems. Her near-term prediction is narrower than much of the hype: by about 2031, agentic AI may handle roughly half of traditional line-one and line-two IT administration tasks, with humans still supervising the loop.
PFF’s Two-Engineer Agent Team Shipped 10x More Output
PFF CTO Mike Spitz argues that AI agents change the basic operating constraint of an engineering organization: the question is no longer how to make engineers faster, but how to make agents faster. In a three-month case study, he says two agent-heavy engineers shipped far more frequently than a ten-person team on the same codebase, with PFF measuring a 10x output gain per engineer and higher customer satisfaction. The result, in his account, was not the end of engineers but the removal of Scrum-era coordination rituals and a sharper split between agent-executed work and human judgment.
AlphaGo Shows How Search Can Turn RL Into Supervised Learning
Eric Jang rebuilds AlphaGo as a way to examine why its combination of search, value learning and self-play still matters for modern AI. His central claim is that AlphaGo’s Monte Carlo Tree Search turns each move into a better supervised-learning target, avoiding the long-horizon credit-assignment problem that makes much reinforcement learning for language models inefficient. Jang also argues that current LLM research assistants can already help execute and optimize experiments, but still struggle with the harder judgment of choosing which research paths are worth pursuing.
AI’s Value Is Moving From SaaS Margins to Hardware Capacity
PwC technology, media and telecommunications leader Dallas Dolen argues that the AI boom is a real infrastructure and business-model shift, but one constrained by chips, construction labor, telecom capacity, copper, power and enterprise economics. In a PwC-sponsored interview, he says value is moving from SaaS toward hardware, software margins are compressing, and most companies are less limited by compute access than by token costs, security rules and measurable return on investment. Dolen’s view of enterprise AI is practical and bounded: agents are working in defined back-office, sales and legal tasks, while broader automation will depend on cost, governance and human oversight.