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Joint SEC-CFTC interpretive guidance outlines how the agencies will determine whether a cryptocurrency is a security.

You felt it last week at the gas station when the numbers on the pump climbed faster than you expected them to.That sudden jolt at the fuel pump is just the opening act of a much larger financial disruption unfolding right now. The cost of filling your tank has surged more than 30% since late February, and the ripple effects are already spreading far beyond the highway.Your grocery cart is directly in the path of this storm, and the connection between crude oil and cereal prices is closer than you might think. The supply chain that puts food on your table runs on diesel, depends on fertilizer, and follows shipping routes that are now under serious threat.Before you plan your next trip to the supermarket, you need to understand exactly where these price pressures come from and how long they could last.The Strait of Hormuz crisis is sending oil prices to four-year highsBrent crude surpassed $100 per barrel on March 8 for the first time since 2022, according to data from the U.S. Energy Information Administration. Prices have since climbed above $108 per barrel as the war between the U.S., Israel, and Iran disrupts the world’s most critical oil chokepoint.The Strait of Hormuz normally handles roughly 20% of all global oil shipments, moving about 20 million barrels through its narrow waters each day. Since the conflict began on February 28, tanker traffic through the Strait has essentially stopped due to Iranian military threats and attacks.Related: It’s not just rising oil prices you’ll have to worry about if Iran conflict continuesThe International Energy Agency has called this the largest disruption to global energy supplies in recorded history, surpassing every prior oil shock event. The IEA and its member nations have authorized the release of 400 million barrels from emergency reserves to try to stabilize markets.That emergency measure has not been enough to calm prices, and the crisis shows no clear signs of resolution in the near term.Gas prices have already jumped more than a dollar in some statesThe national average price for a gallon of regular gasoline hit $3.88 as of March 19, according to AAA’s daily fuel price tracker. That represents a rise of more than $1 per gallon from the $2.81 average recorded in early January 2026.California drivers are paying $5.62 per gallon, while even the cheapest states like Oklahoma and Kansas now sit above $3.20 per gallon.More Oil and Gas:Energy giant sends blunt $20 billion message on dividend growth147-year-old oil giant just raised dividend 4% in 2026Top energy stocks to buy amid Venezuela chaosThe U.S. Energy Information Administration projects that gasoline could average $3.34 per gallon for all of 2026, a sharp upward revision from the $2.91 forecast in February.For a household that drives 12,000 miles per year, even a $0.80-per-gallon increase translates to roughly $400 in extra annual fuel costs. If you commute 30 miles each way to work, your monthly gas bill could rise by $50 to $75, depending on your vehicle.How diesel costs and fertilizer shortages could push your grocery bill higherEvery product on your grocery store shelf arrives by truck, train, or cargo ship, and all three modes of transport run on diesel fuel. When oil prices spike, the cost of moving food from farm to shelf rises at every single stage of the supply chain.Perishable items such as dairy, fresh produce, meat, and seafood are expected to see price increases first, CNN Business reported. These products require temperature-controlled transport, which consumes more fuel than standard dry freight shipping.The fertilizer problem is even more concerningAbout 35% of the world’s urea and between 20% and 30% of global fertilizer exports pass through the Strait of Hormuz, according to the Center for Strategic and International Studies.U.S. ammonia prices are already 41% higher than they were last March, and urea prices have risen 21%, NBC News reported, citing RSM chief economist Joe Brusuelas. Countries affected by the Hormuz disruption account for roughly 49% of global urea exports.The spring planting season is underway across the United States, which means farmers are buying fertilizer and applying nutrients now. Any disruption to these supplies during this critical window could push food prices higher for months to come.The inflation progress you gained in 2025 is now under direct threatHeadline consumer inflation had been cooling steadily since its 9.1% peak in June 2022, falling to 2.4% by January 2026, according to the Bureau of Labor Statistics. The latest CPI data showed grocery prices rose 0.4% from January to February, putting them 2.4% above year-ago levels.Goldman Sachs economists have warned that energy costs make up 6.4% of the headline CPI calculation, meaning sustained high oil prices could visibly push overall inflation readings higher. A 20% surge in gasoline alone can move the headline number, even when underlying trends remain stable.IMF Managing Director Kristalina Georgieva has warned that the oil shock could effectively undo recent inflation progress and slow global economic growth. For the Federal Reserve, this complicates any plans for interest rate cuts later in 2026, which directly affects your mortgage rate, your car loan, and your credit card APR.When the price shock could show up in your shopping cartEconomists describe an asymmetry called the “rockets-and-feathers” pattern, where prices rise like a rocket when oil surges but float down like a feather after the shock passes. Pump prices respond more than twice as quickly to oil price increases as they do to decreases, according to documented economic research.Here is the rough timeline experts are watchingRight now: Gas prices are already elevated, and diesel costs are climbing across the freight and logistics industry nationwide.Within 2 to 4 weeks: Perishable grocery items such as dairy, produce, and meat could begin reflecting higher transportation costs.Within 2 to 6 months: Fertilizer-driven cost increases could push grain, cereal, and processed food prices higher as the planting season progresses.Ongoing risk: If the Strait of Hormuz remains effectively closed for months, the EIA projects gasoline may not fall below $3 per gallon through 2027.The duration of this conflict is the single most important variable determining how much you ultimately pay at the grocery store and pump.

Understanding how oil prices affect everyday expenses helps households prepare for rising costs and adjust their budgets ahead of time.Hryshchyshen Serhii/Shutterstock

Shrinkflation and layoff risks could compound the price pressureWhen oil prices surged during the Russia-Ukraine conflict in 2022, many companies responded by reducing product sizes while keeping prices the same. This practice, widely known as shrinkflation, is effectively a hidden price increase that consumers often do not notice right away.Related: Oil shock sends blunt message on stock market inflation riskPersistent high oil prices will produce a sustained cost shock across the economy, Boston College Economics Professor Brian Bethune told CNN Business. With businesses already absorbing tariff-related costs, many have little room to absorb higher transportation expenses without passing them to you.If companies cannot raise prices or shrink packages without losing customers, the next option is to reduce headcount to cut operating costs. Consumers who are already pulling back on spending may force businesses into that harder choice sooner than most analysts currently expect.Smart moves to protect your household budgetYou cannot control oil prices or geopolitical conflicts, but you can take concrete steps to reduce the financial impact on your household. The following strategies target the areas most exposed to rising energy and food costs.Reduce your fuel costs where possibleConsolidate errands into fewer trips to cut total miles driven and reduce your weekly fuel consumption by a meaningful amount.Use gas price comparison apps like GasBuddy to find the lowest prices near your commute route before you fill up the tank.Consider using a credit card that offers elevated cash-back rewards on gas purchases to offset some of the extra cost at the pump.Adjust your grocery strategy before prices climb furtherStock up on shelf-stable staples like rice, pasta, canned goods, and frozen vegetables before transportation cost increases hit store shelves.Shift toward seasonal and locally sourced produce, which requires shorter transportation distances and is less exposed to diesel surges.Compare unit prices rather than sticker prices, because shrinkflation means the package size may have changed without a visible label update.Plan your weekly meals in advance to reduce impulse buying and food waste, which together account for significant household spending leaks.Revisit your broader household budgetReview your fixed monthly expenses to identify subscriptions, memberships, or services you can pause until price pressures stabilize further ahead.Build or replenish your emergency fund if possible, because energy-driven inflation tends to squeeze household budgets from multiple directions at once.Avoid making large discretionary purchases on credit right now, since the Federal Reserve is less likely to cut interest rates under current conditions.The road ahead depends on the Strait of HormuzThe fundamental question driving every price forecast is how quickly tanker traffic through the Strait of Hormuz can resume at normal volumes. Goldman Sachs is modeling roughly 21 days of severely reduced flows followed by a 30-day recovery period, but that scenario could easily prove too optimistic.Israeli Prime Minister Benjamin Netanyahu said on March 19 that Israel was helping the U.S. reopen the Strait and suggested the war could end sooner than expected, CNBC reported. However, Iran’s new supreme leader, Mojtaba Khamenei, has vowed to keep the Strait closed as a tool to pressure adversaries.For your household, the practical takeaway is straightforward but important: Plan for higher prices that will last at least several months, not just weeks. The EIA’s latest projection does not see gasoline prices falling below $3 per gallon at any point between now and the end of 2027.If the conflict resolves quickly, prices should moderate, but the rockets-and-feathers pattern means the descent will be much slower than the spike. Prepare your budget for the elevated cost environment, and adjust your spending before the second wave of price increases from fertilizer disruptions hits your grocery bill.Related: Iran’s shocking threat to boost oil to $200​

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Look, we’ve spent the last 18 months building production AI systems, and we’ll tell you what keeps us up at night — and it’s not whether the model can answer questions. That’s table stakes now. What haunts us is the mental image of an agent autonomously approving a six-figure vendor contract at 2 a.m. because someone typo’d a config file.We’ve moved past the era of “ChatGPT wrappers” (thank God), but the industry still treats autonomous agents like they’re just chatbots with API access. They’re not. When you give an AI system the ability to take actions without human confirmation, you’re crossing a fundamental threshold. You’re not building a helpful assistant anymore — you’re building something closer to an employee. And that changes everything about how we need to engineer these systems.The autonomy problem nobody talks aboutHere’s what’s wild: We’ve gotten really good at making models that *sound* confident. But confidence and reliability aren’t the same thing, and the gap between them is where production systems go to die.We learned this the hard way during a pilot program where we let an AI agent manage calendar scheduling across executive teams. Seems simple, right? The agent could check availability, send invites, handle conflicts. Except, one Monday morning, it rescheduled a board meeting because it interpreted “let’s push this if we need to” in a Slack message as an actual directive. The model wasn’t wrong in its interpretation — it was plausible. But plausible isn’t good enough when you’re dealing with autonomy.That incident taught us something crucial: The challenge isn’t building agents that work most of the time. It’s building agents that fail gracefully, know their limitations, and have the circuit breakers to prevent catastrophic mistakes.What reliability actually means for autonomous systemsLayered reliability architectureWhen we talk about reliability in traditional software engineering, we’ve got decades of patterns: Redundancy, retries, idempotency, graceful degradation. But AI agents break a lot of our assumptions.Traditional software fails in predictable ways. You can write unit tests. You can trace execution paths. With AI agents, you’re dealing with probabilistic systems making judgment calls. A bug isn’t just a logic error—it’s the model hallucinating a plausible-sounding but completely fabricated API endpoint, or misinterpreting context in a way that technically parses but completely misses the human intent.So what does reliability look like here? In our experience, it’s a layered approach.Layer 1: Model selection and prompt engineeringThis is foundational but insufficient. Yes, use the best model you can afford. Yes, craft your prompts carefully with examples and constraints. But don’t fool yourself into thinking that a great prompt is enough. I’ve seen too many teams ship “GPT-4 with a really good system prompt” and call it enterprise-ready.Layer 2: Deterministic guardrailsBefore the model does anything irreversible, run it through hard checks. Is it trying to access a resource it shouldn’t? Is the action within acceptable parameters? We’re talking old-school validation logic — regex, schema validation, allowlists. It’s not sexy, but it’s effective.One pattern that’s worked well for us: Maintain a formal action schema. Every action an agent can take has a defined structure, required fields, and validation rules. The agent proposes actions in this schema, and we validate before execution. If validation fails, we don’t just block it — we feed the validation errors back to the agent and let it try again with context about what went wrong.Layer 3: Confidence and uncertainty quantificationHere’s where it gets interesting. We need agents that know what they don’t know. We’ve been experimenting with agents that can explicitly reason about their confidence before taking actions. Not just a probability score, but actual articulated uncertainty: “I’m interpreting this email as a request to delay the project, but the phrasing is ambiguous and could also mean…”This doesn’t prevent all mistakes, but it creates natural breakpoints where you can inject human oversight. High-confidence actions go through automatically. Medium-confidence actions get flagged for review. Low-confidence actions get blocked with an explanation.Layer 4: Observability and auditabilityAction Validation Pipeline If you can’t debug it, you can’t trust it. Every decision the agent makes needs to be loggable, traceable, and explainable. Not just “what action did it take” but “what was it thinking, what data did it consider, what was the reasoning chain?”We’ve built a custom logging system that captures the full large language model (LLM) interaction — the prompt, the response, the context window, even the model temperature settings. It’s verbose as hell, but when something goes wrong (and it will), you need to be able to reconstruct exactly what happened. Plus, this becomes your dataset for fine-tuning and improvement.Guardrails: The art of saying noLet’s talk about guardrails, because this is where engineering discipline really matters. A lot of teams approach guardrails as an afterthought — “we’ll add some safety checks if we need them.” That’s backwards. Guardrails should be your starting point.We think of guardrails in three categories.Permission boundariesWhat is the agent physically allowed to do? This is your blast radius control. Even if the agent hallucinates the worst possible action, what’s the maximum damage it can cause?We use a principle called “graduated autonomy.” New agents start with read-only access. As they prove reliable, they graduate to low-risk writes (creating calendar events, sending internal messages). High-risk actions (financial transactions, external communications, data deletion) either require explicit human approval or are simply off-limits.One technique that’s worked well: Action cost budgets. Each agent has a daily “budget” denominated in some unit of risk or cost. Reading a database record costs 1 unit. Sending an email costs 10. Initiating a vendor payment costs 1,000. The agent can operate autonomously until it exhausts its budget; then, it needs human intervention. This creates a natural throttle on potentially problematic behavior.Graduated Autonomy and Action Cost Budget Semantic HoundariesWhat should the agent understand as in-scope vs out-of-scope? This is trickier because it’s conceptual, not just technical.I’ve found that explicit domain definitions help a lot. Our customer service agent has a clear mandate: handle product questions, process returns, escalate complaints. Anything outside that domain — someone asking for investment advice, technical support for third-party products, personal favors — gets a polite deflection and escalation.The challenge is making these boundaries robust to prompt injection and jailbreaking attempts. Users will try to convince the agent to help with out-of-scope requests. Other parts of the system might inadvertently pass instructions that override the agent’s boundaries. You need multiple layers of defense here.Operational boundariesHow much can the agent do, and how fast? This is your rate limiting and resource control.We’ve implemented hard limits on everything: API calls per minute, maximum tokens per interaction, maximum cost per day, maximum number of retries before human escalation. These might seem like artificial constraints, but they’re essential for preventing runaway behavior.We once saw an agent get stuck in a loop trying to resolve a scheduling conflict. It kept proposing times, getting rejections, and trying again. Without rate limits, it sent 300 calendar invites in an hour. With proper operational boundaries, it would’ve hit a threshold and escalated to a human after attempt number 5.Agents need their own style of testingTraditional software testing doesn’t cut it for autonomous agents. You can’t just write test cases that cover all the edge cases, because with LLMs, everything is an edge case.What’s worked for us:Simulation environmentsBuild a sandbox that mirrors production but with fake data and mock services. Let the agent run wild. See what breaks. We do this continuously — every code change goes through 100 simulated scenarios before it touches production.The key is making scenarios realistic. Don’t just test happy paths. Simulate angry customers, ambiguous requests, contradictory information, system outages. Throw in some adversarial examples. If your agent can’t handle a test environment where things go wrong, it definitely can’t handle production.Red teamingGet creative people to try to break your agent. Not just security researchers, but domain experts who understand the business logic. Some of our best improvements came from sales team members who tried to “trick” the agent into doing things it shouldn’t.Shadow modeBefore you go live, run the agent in shadow mode alongside humans. The agent makes decisions, but humans actually execute the actions. You log both the agent’s choices and the human’s choices, and you analyze the delta.This is painful and slow, but it’s worth it. You’ll find all kinds of subtle misalignments you’d never catch in testing. Maybe the agent technically gets the right answer, but with phrasing that violates company tone guidelines. Maybe it makes legally correct but ethically questionable decisions. Shadow mode surfaces these issues before they become real problems.The human-in-the-loop patternThree Human-in-the-Loop Patterns Despite all the automation, humans remain essential. The question is: Where in the loop?We’re increasingly convinced that “human-in-the-loop” is actually several distinct patterns:Human-on-the-loop: The agent operates autonomously, but humans monitor dashboards and can intervene. This is your steady-state for well-understood, low-risk operations.Human-in-the-loop: The agent proposes actions, humans approve them. This is your training wheels mode while the agent proves itself, and your permanent mode for high-risk operations.Human-with-the-loop: Agent and human collaborate in real-time, each handling the parts they’re better at. The agent does the grunt work, the human does the judgment calls.The trick is making these transitions smooth. An agent shouldn’t feel like a completely different system when you move from autonomous to supervised mode. Interfaces, logging, and escalation paths should all be consistent.Failure modes and recoveryLet’s be honest: Your agent will fail. The question is whether it fails gracefully or catastrophically.We classify failures into three categories:Recoverable errors: The agent tries to do something, it doesn’t work, the agent realizes it didn’t work and tries something else. This is fine. This is how complex systems operate. As long as the agent isn’t making things worse, let it retry with exponential backoff.Detectable failures: The agent does something wrong, but monitoring systems catch it before significant damage occurs. This is where your guardrails and observability pay off. The agent gets rolled back, humans investigate, you patch the issue.Undetectable failures: The agent does something wrong, and nobody notices until much later. These are the scary ones. Maybe it’s been misinterpreting customer requests for weeks. Maybe it’s been making subtly incorrect data entries. These accumulate into systemic issues.The defense against undetectable failures is regular auditing. We randomly sample agent actions and have humans review them. Not just pass/fail, but detailed analysis. Is the agent showing any drift in behavior? Are there patterns in its mistakes? Is it developing any concerning tendencies?The cost-performance tradeoffHere’s something nobody talks about enough: reliability is expensive.Every guardrail adds latency. Every validation step costs compute. Multiple model calls for confidence checking multiply your API costs. Comprehensive logging generates massive data volumes.You have to be strategic about where you invest. Not every agent needs the same level of reliability. A marketing copy generator can be looser than a financial transaction processor. A scheduling assistant can retry more liberally than a code deployment system.We use a risk-based approach. High-risk agents get all the safeguards, multiple validation layers, extensive monitoring. Lower-risk agents get lighter-weight protections. The key is being explicit about these trade-offs and documenting why each agent has the guardrails it does.Organizational challengesWe’d be remiss if we didn’t mention that the hardest parts aren’t technical — they’re organizational.Who owns the agent when it makes a mistake? Is it the engineering team that built it? The business unit that deployed it? The person who was supposed to be supervising it?How do you handle edge cases where the agent’s logic is technically correct but contextually inappropriate? If the agent follows its rules but violates an unwritten norm, who’s at fault?What’s your incident response process when an agent goes rogue? Traditional runbooks assume human operators making mistakes. How do you adapt these for autonomous systems?These questions don’t have universal answers, but they need to be addressed before you deploy. Clear ownership, documented escalation paths, and well-defined success metrics are just as important as the technical architecture.Where we go from hereThe industry is still figuring this out. There’s no established playbook for building reliable autonomous agents. We’re all learning in production, and that’s both exciting and terrifying.What we know for sure: The teams that succeed will be the ones who treat this as an engineering discipline, not just an AI problem. You need traditional software engineering rigor — testing, monitoring, incident response — combined with new techniques specific to probabilistic systems.You need to be paranoid but not paralyzed. Yes, autonomous agents can fail in spectacular ways. But with proper guardrails, they can also handle enormous workloads with superhuman consistency. The key is respecting the risks while embracing the possibilities.We’ll leave you with this: Every time we deploy a new autonomous capability, we run a pre-mortem. We imagine it’s six months from now and the agent has caused a significant incident. What happened? What warning signs did we miss? What guardrails failed?This exercise has saved us more times than we can count. It forces you to think through failure modes before they occur, to build defenses before you need them, to question assumptions before they bite you.Because in the end, building enterprise-grade autonomous AI agents isn’t about making systems that work perfectly. It’s about making systems that fail safely, recover gracefully, and learn continuously.And that’s the kind of engineering that actually matters.Madhvesh Kumar is a principal engineer. Deepika Singh is a senior software engineer. Views expressed are based on hands-on experience building and deploying autonomous agents, along with the occasional 3 AM incident response that makes you question your career choices.

While upgrades have improved efficiency and lowered costs, the ecosystem faces deeper structural questions around fragmentation, security, and purpose, even as it continues prioritizing base-layer scaling.

Popular theme parks have begun disappearing from the landscape, as developers anticipate huge returns from converting massive amusement park parcels of land into lucrative real estate projects.Cedar Fair sold its valuable California Great America property in the Silicon Valley to real estate developer Prologis in 2022, before it merged with Six Flags in 2024.California Great America announced it will close its gates permanently at the end of the 2027 amusement park season.

Andretti Thrill Park will close for redevelopment after 27 years of operation. Shutterstock

Andretti Thrill Park closes downAnd now, another beloved family theme park, Andretti Thrill Park, which was co-founded by the late NASCAR driver John Andretti in 1999, has closed down permanently and will be redeveloped into an apartment complex with over 300 units, The Space Coast Rocket reported.The Melbourne, Fla.-based race car-themed thrill park’s owner Eddie Hamann, who co-founded the theme park with Andretti, said economic issues were not the reason for closing the park, as it was performing well financially.Co-Founder John Andretti passed awayAndretti passed away from colon cancer in 2020. John Andretti was the nephew of car-racing legend Mario Andretti.”There was no issue with the city, no issue with the county. Sales were still strong. We never lost money here,” Hamann said, according to The Space Coast Rocket.”It’s simply that the facility is 27 years old. It requires a lot of maintenance, and it was time to look at what the future holds,” Hamann said.A high demand for housing in the Melbourne area prompted the decision to redevelop the aging 16.7-acre property into apartments for students at the Florida Institute of Technology and local workers, the thrill park owner said.Andretti Thrill Park had become a popular entertainment venue for local families on the Space Coast, offering a variety of attractions, including 5 go-kart tracks, 18-hole miniature golf, laser tag arena, paddle boat rides, rock wall, Andretti 360 ride, drop tower, train ride, kiddie rides, and the Space Coast’s largest video arcade with 150 games, according to Trip Advisor.The thrill park’s website and social media sites have already been disabled.Another Andretti karting chain operatesHamann is also a partner in another Andretti family venture, Andretti Indoor Karting & Games. The go-karting and arcade chain has 14 locations in 8 states, which are not affiliated with Andretti Thrill Park.Fans of 7 Six Flags amusement parks will be relieved to know their favorite park destinations, however, will still be operating after the 2026 season ends.EPR Properties buys Six Flags parksEPR Properties purchased the parks for $342 million with plans to sign long-term leases with major theme park operators Enchanted Parks and La Ronde Operations Inc., the company announced on March 5.”This strategic acquisition represents a compelling opportunity to expand our attractions portfolio with high-quality experiential real estate assets in established regional markets,” Gregory K. Silvers, chairman and chief executive officer of EPR Properties, said in a statement.”These properties embody the essential characteristics we seek: delivering stable, long-term cash flows, strong drive-to accessibility, multi-generational appeal, and significant underlying land value” Silvers said.”This transaction aligns with our disciplined investment criteria and accelerates our strategic expansion into experiential properties that create enduring value for our shareholders,” he said.Six Flags Parks Sold to EPR PropertiesWorld of Fun, Kansas City, Mo., leased to Enchanted Parks.Valleyfair, Minneapolis, Minn., leased to Enchanted Parks.Six Flags St. Louis, Mo., leased to Enchanted Parks.Schlitterbahn Waterpark Galveston, Galveston, Texas, leased to Enchanted Parks.Michigan Adventure, Grand Rapids, Mich., leased to Enchanted Parks.Six Flags Great Escape, Queensbury, N.Y., leased to Enchanted Parks.Six Flags La Ronde, Queensbury, N.Y., leased to La Ronde Operations Inc. Related: Major fried chicken franchisee closes in Chapter 11 bankruptcy

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Paramount and Warner Bros. Discovery will own the most valuable library of television shows with 40% of viewing to the top acquired shows on streaming.

Episode eight of ‘The Amazing Digital Circus’ reveals the origin of Caine and hints at the true purpose of the Circus—here are all of the twists, explained.