SUCCESS

All four favorites advanced in straight matches in the first round of the 2026 Major League Pickleball Playoffs, but not without some drama.

Content filters can block unsafe output. They cannot tell you whether an agent was authorized to issue that refund, touch that production system, or commit the company to an external action. Those are different problems, and most enterprises are only solving the first one.An AI agent can follow its instructions perfectly and still take an action the business never sanctioned.In commerce environments, I have seen this pattern emerge in practical ways. A service workflow calculates the correct refund amount but lacks a boundary preventing credits above what the business approved for autonomous action. An order agent correctly applies a requested change but overlooks a financing or fulfillment condition. A procurement agent identifies the lowest-cost supplier, but nobody has defined whether it can accept contractual terms or only recommend the option.The agent keeps working. The problem may not surface until something downstream breaks.These are not necessarily AI reasoning failures. They are failures to separate technical capability from business authority.As enterprises move from copilots that recommend to agents that call tools and trigger workflows, every production agent needs explicit decision rights: What it may execute, what requires approval, what it may only recommend, and what it must never touch.Guardrails remain necessary. But a guardrail is not an authority model.Safety controls and decision rights solve different problemsEarly gen AI controls screen harmful content, protect sensitive information, validate responses, and constrain tool behavior. That work matters.Decision rights answer a different question: Even when an action is safe and technically valid, is this agent authorized to take it on behalf of the enterprise?That governance gap is becoming harder to ignore. In April 2026, a Cloud Security Alliance survey found that 65% of respondents had experienced an AI-agent-related incident in the prior year, while 82% had discovered previously unknown agents operating in their environments. The survey involved 418 IT and security professionals and was sponsored by Token Security.The findings illustrate how quickly agent activity can outpace the visibility and ownership structures built for conventional software.The World Economic Forum’s May 2026 playbook reflects this shift. It introduces an Agent Capability and Authorization Profile designed to make delegated actions auditable, enforceable and accountable.Guardrails constrain behavior. Decision rights define legitimate authority.Give every production agent an authority contractBefore an agent receives access to enterprise tools, it needs a machine-enforceable record of exactly what authority the business has chosen to delegate. Call it an Agent Authority Contract.At minimum, that contract should answer seven questions:Who owns the outcome? Name a human or business role, not another system.What may the agent do? Read, recommend, write, or commit?Which systems and data may it reach?What materiality limits apply? Define dollar thresholds, record counts, customer scope, and operational impact.What triggers escalation? Uncertainty, anomaly, sensitive data, or potential impact?Can the action be reversed, and who can reverse it?When does the authority expire, and how is it withdrawn?Access control determines whether an agent can reach a system. The authority contract determines whether it may take a specific action in the current context.Those are not the same check.Singapore’s updated Model AI Governance Framework for Agentic AI draws a similar distinction. It treats access controls, behavioral guardrails, and human approvals as separate controls and ties oversight requirements to action scope, reversibility and potential impact.Resolve every consequential action into four outcomesA working decision-rights model should map every consequential agent action to one of four results.AllowLow-risk, bounded, and reversible actions run autonomously.Examples include retrieving approved information, classifying an inbound request, or updating a non-material field. The agent acts without prior review because the potential impact is limited and the action can be reversed.ApproveThe agent prepares or initiates the action, but execution waits for authorization from a human or deterministic policy service.This category covers payments, production changes, and actions that materially affect a customer, employee, or third party.RecommendThe agent analyzes, ranks, drafts, or proposes. A named human makes the final decision.Use this outcome when contextual judgment matters or when the legal, financial, or individual impact makes automated execution unacceptable.DenyThe action remains outside the agent’s authority regardless of its confidence.Deleting critical production data, making a final employment decision or overriding a mandatory compliance control should remain in the Deny category even when the agent’s underlying reasoning appears correct.One point gets missed consistently: Deny must be enforced outside the system prompt.A natural-language instruction telling an agent not to do something is not a technical boundary. It is a suggestion.Make authority decisions at runtimeStatic configuration cannot cover every situation.A small service credit might be allowed under normal conditions but require approval when the amount crosses a threshold, the account is under investigation, or the request involves a regulated customer.A practical runtime sequence looks like this:The agent proposes an action.A policy layer evaluates the agent’s identity, delegated principal, requested tool, data involved, transaction context, and potential impact.The policy returns Allow, Approve, Recommend, or Deny.The system records the authority decision, resulting action and outcome.Operational telemetry expands, narrows, or revokes the agent’s authority over time.In enterprise commerce, the most dangerous AI mistake is not always a false answer. It can be a technically correct action the agent had no business taking.A refund may be accurate but exceed an approval limit. An order change may match the customer’s request but invalidate a financing condition. A delivery promise may reflect available inventory while overlooking a carrier constraint applied an hour earlier.The agent may not have failed to reason. The enterprise failed to define where its authority stopped.Human oversight should target exceptions, not everythingRequiring human approval for every agent action looks conservative. At scale, it can quickly degrade into rubber-stamping.When reviewers approve thousands of routine actions, attention declines and genuine exceptions become harder to identify. Singapore’s framework acknowledges that continuous human oversight of every agent workflow becomes impractical at scale and recommends meaningful checkpoints for higher-risk or irreversible actions.Proportional authorization is the more workable model.Low-risk actions run within narrow boundaries. High-risk or irreversible actions require approval. Unexpected behavior triggers escalation. Any consequential action without a defined authorization policy is denied by default.The objective is not maximum autonomy. It is the highest level of autonomy the enterprise can observe, govern and reverse responsibly.Measure whether authority is calibratedOnce agents are in production, response accuracy becomes too narrow a success metric.Enterprises should also track:Override rate: How often do humans reject or materially change what the agent decided?Escalation precision: Does the agent surface genuinely risky cases, or does it return routine work to people?Unauthorized-action attempts: How often does the agent try to exceed its system, data, or action scope?Business-impacting error rate: How often do authorized actions produce financial, compliance, operational, or customer harm?Decision latency: Are approval requirements managing risk, or slowing down automation that was already safe?These measures turn authority into a governed operating variable.Consistently reliable performance may justify expanding bounded authority. Frequent overrides, escalation failures, or policy violations should narrow it.The governance gap is not in the modelModel safety, output controls, and secure tool use all matter. Enterprises should continue investing in them.But none of those controls can answer who delegated authority, how much was transferred, under what conditions it applies, or who owns the result when something goes wrong.An Agent Authority Contract can.Before asking how autonomous an AI agent can become, the more useful question is: What is the enterprise actually prepared to delegate, and how will that delegation be enforced, observed, and withdrawn?The agent demo works. That is not the hard part anymore.Nixal Patel is a product leader. The views expressed are his own

The New York Yankees have emerged as a landing spot for a Boston Red Sox veteran who left the team amid a strong hitting surge.

TrueFans.fm is a revolutionary portal, not just an app, designed as a secure, centralized gateway for independent podcasters, listeners, and advertisers.

The company increased its USD reserve to $4.65 billion while reducing its bitcoin holdings to 840,447 BTC

Tax loss harvesting is a very effective strategy for investors to trim their tax bill each year. Selling underperforming holdings in a taxable account for a loss can help offset gains realized elsewhere. You can use losses to offset realized capital gains and can use up to $3,000 of capital losses to offset other income each year.One potential roadblock, though, is the IRS wash sale rule (IRS section 1091).What is the wash sale rule?The wash sale rule disallows the loss if you purchase the same or a “substantially identical” security within a 61-day window (30 days before the sale, the day of the sale, and 30 days after the sale). This is to prevent investors from “gaming the system” by selling a holding for a loss, taking the tax write-off and immediately rebuying the identical security at a lower price.The wash sale rule applies in many cases that investors might not consider, including:Purchasing the same or a substantially identical security in a spouse’s account within the prohibited time frame is a violation.Purchasing the same or a substantially identical security in a tax-deferred account like an IRA is a violation.A new calendar year does not impact the 61 day timeline.This can create a serious dilemma for some investors: Sell the security for a tax loss, but then potentially miss a rebound in the price of the security during the 30 days following the sale.Related: Vanguard renames key funds to highlight Morningstar benchmarksWhat does “substantially identical” mean?The concept of substantially identical is a key one, and one that can be vague. If you were to sell shares of Intuit stock at a loss then immediately rebuy shares in Intuit, that is pretty clear. Likewise if you were to sell and then rebuy shares of the Vanguard S&P 500 ETF (ticker VOO).Where it can get vague is if you sold shares of VOO at a loss and then bought shares of another ETF or mutual fund that tracks the same index.

Bessent claims U.S. economic growth could exceed 3% despite global pessimism. Shutterstock

ETFs can aid in tax loss harvesting, which can ultimately help investors reduce their taxes.How can ETFs help you avoid violating the wash sale rule?ETFs can offer a viable strategy for investors looking to avoid the wash sale rule in several ways.  Selling an individual stock and buying an ETF in the same sectorLet’s say an investor sells shares of Pfizer stock (ticker PFE) at a loss. Instead of purchasing shares of Pfizer and invoking the wash sale rule, they could buy shares of an ETF that tracks the healthcare sector such as the Vanguard Healthcare Index Fund ETF Shares (ticker VHT). This allows you to lock in the tax loss and related tax deduction while staying invested in the healthcare sector. A diversified ETF is not considered substantially identical to shares of an individual stock.Selling a benchmark index fund and buying a different index ETFThis scenario would involve selling an index mutual fund or ETF at a loss and immediately buying shares of an ETF tracking a different index but with similar holdings and performance characteristics. An example might be selling shares of the State Street® SPDR® S&P 500® ETF Trust (ticker SPY) at a loss and purchasing shares of the Vanguard Morningstar Total Stock Market ETF (ticker VTI).VTI tracks a different index and is a bit broader in scope than the S&P, but the two funds share many holdings and have many similar performance characteristics.Sell an active mutual fund and purchase a passive ETFAnother scenario might involve an investor selling an actively managed mutual fund at a loss and then buying shares of a passive ETF in the same asset class. For example, if an investor sold shares of The Growth Fund of America (ticker AGTHX), an actively managed large cap growth fund at a loss, and then purchased shares of the Vanguard Morningstar Growth ETF (ticker VUG) a passive large cap growth ETF, this would allow them to realize the loss on AGTHX while remaining invested in an ETF that is also in the large cap growth asset class.Two critical pitfalls to avoidWhile these ETF strategies are legal and widely used by institutional wealth managers, individual investors can easily get tripped up over subtle landmines:Dividend Reinvestments (DRIP): Dividend Reinvestment Plans (DRIPs) inside the 61-day window can trigger a partial wash sale automatically if a dividend reinvests into the security you recently sold for a loss. Be sure to turn off auto-reinvestments on tax-harvested positions.Cross–account activity: The wash sale rules apply across all accounts you and your spouse own. If you sell an ETF, mutual fund, or other security at a loss in a taxable individual account and purchase the same security in your IRA or 401(k) within 30 days, the loss is permanently disallowed.Related: Popular ETFs carry hidden tax rules that surprise retail investors

Applied Materials has benefited from the latest tech trend in artificial intelligence. The Santa Clara, California-based semiconductor manufacturer builds AI chips used in data centers, and its stock has risen significantly in recent years. The company posted nearly $7 billion in net income on record revenue of $28.4 billion in fiscal 2025, and has returned some of that profit to shareholders through dividends.Here’s a closer look at Applied Materials’ dividend history and how its payouts increased over the years.Applied Materials dividend quick factsCurrent quarterly payout:$0.53 per shareCurrent annual payout (estimated fiscal 2026): $2.05 per shareYield, 12-month trailing dividend yield: 0.36%Payout ratio: 20%Frequency: QuarterlyYears of consecutive dividend increases: 16Based on Applied Materials’ August 6, 2026 stock price, fiscal 2025 and nine-month 2026 earnings and dividends.How often does Applied Materials pay dividends?Applied Materials pays a cash dividend on a quarterly basis. Its fiscal year follows a 52- or 53-week schedule, starting in late October and ending on the last Sunday in October of the following calendar year.Dividend payments are typically made in the middle of March, June, September, and December. What is Applied Materials’ dividend yield?Applied Materials’ 12-month trailing dividend yield was 0.36% as of early August 2026. That’s based on the 12-month dividend payment of $1.91 for the quarters in September and December of 2025, and March and June of 2026, and the August 6, 2026, closing price of $527.48.By comparison, the estimated trailing 12-month average dividend yield for stocks in the S&P 500 index was 1.04%, according to data compiled by multpl.comWhat is Applied Materials’ dividend payout ratio?In fiscal 2025, Applied Materials paid total dividends of $1.72 per share, against net income of $8.66 per share. That put its dividend payout ratio — calculated by dividing the annual dividend per share by the earnings per share — at nearly 20%. By comparison, the average 2025 payout ratio for companies in the S&P 500 was 32.28%, according to data compiled by the Hartford Funds, an asset management firm.When did Applied Materials start paying dividends?Applied Materials was founded in 1967 in Mountain View, California, and went public in 1972, but it would not pay its first dividend until 2006. Its initial way of returning value to shareholders was via stock splits, and its last stock split was in April 2002 — just toward the tail end of the dot-com bust. With its stock trading in a narrow range following the end of the dot-com bubble, its run of stock splits starting in 1980 came to an end. Applied began its first quarterly cash dividend payment on June 8, 2005, at 3 cents per share.More on dividends:Alphabet’s dividend history, yield & future prospects explainedDoes Walmart pay dividends? Its yield & payouts explainedOracle’s dividends: History, yield & payout ratio explainedApplied Materials’ dividend increasesApplied Materials paid its first quarterly dividend of 3 cents a share in June 2005, and quickly raised it to 5 cents a year later. By June 2013, the quarterly dividend doubled to 10 cents a share, and the total dividend for 2013 amounted to 39 cents. Soon, Applied made significant increases in its dividends as revenue and profit increased. In 2019, the chipmaker was making an annual payment of 83 cents, rising to $1.78 by 2025.Applied last increased its quarterly dividend in June 2026, to 53 cents, from 46 cents in March.Who benefits the most from Applied Materials’ dividend?Based on Applied’s 2026 proxy statement, institutional investors BlackRock and The Vanguard Group held 76.8 million shares and 74.1 million shares, respectively, as of the end of 2025. Among Applied’s executives, CEO Gary E. Dickerson held the largest number of shares, with 1,398,872.Related: Does IBM pay dividends? History, yield & payout ratio explained

Onchain forensics tie a single actor to an $8 million coordinated drain across TRON and Ethereum, with most funds routed through FixedFloat and the attack vector still unknown.

The FCA is expected to announce progress on drafting new rules for tokenized digital assets within the next few months.

Your day-ahead look for Aug. 10, 2026