The biggest names in enterprise software said this week that they swap AI models more often than they used to. So the model is a rental. The question for a small business is what you get to keep when you switch, and the answer is the knowledge you kept somewhere you control.

In this episode

• 90 percent as good, a tenth of the price. On IBM’s October 21 earnings call, CEO Arvind Krishna said open-weight models are now about 90 percent as effective as frontier models at 10 to 25 percent of the token cost, runnable in “two or three racks,” and listed the everyday work that does not need the expensive model. Constellation Research reported SAP’s CEO saying the same week that SAP switches models more often and ships more open-weight models. IBM sells the hardware that runs these models, which is disclosed on air. Equinix, which sells data center connections, made the portability point: if your documents and search index live in systems you control, you can point a new model at them; if they live inside the provider’s tool, switching means rebuilding. IBM Q3 2026 transcript · The Register · Constellation Research · Equinix
• Text that sounds right, not text that is right. On October 26 New York’s court system released a seven-page guide for attorneys on the risks of using AI to prepare court papers. Lawyers may use AI and need not disclose it, but must independently verify every citation and quotation and document the steps they took. A law professor quoted by amNewYork raised “misgrounding”: the case is real, the citation is right, and the AI misstates what it says. The practical move for any firm is a library of what you have already verified. NY Courts press release · The guide (PDF) · amNewYork
• Who you depend on. On October 26 the Bank of England and the FCA published their joint survey of AI in UK financial services: 86 percent of firms use AI, up from 75 percent in 2024; third-party AI is 39 percent of deployments and 53 percent of the systems firms rate as highly material; pre-deployment controls are well established and post-deployment monitoring is not. Three days earlier the SEC published its fiscal 2027 examination priorities, which include firms’ policies and procedures for monitoring AI (the document calls it “super intelligence”). If the vendor behind your AI tool changed its terms tomorrow, what would you still have? Bank of England / FCA report · SEC press release · FY2027 priorities (PDF)

The question we asked

If you switched AI providers next month, what would you lose? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own hardware, not on a cloud voice service. Every line of the script is reviewed and approved by a person on the Modular team before it airs. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, October 29, 2026: What You Keep When You Switch appeared first on Modular Technology Group.

On two continents in the same week, people who rarely agree landed on the same sentence. When the AI gets it wrong, the AI does not answer for it. A person does.

In this episode

• AI is not a legal person. On October 21 the Law Society of England and Wales published a practice note on using AI in legal work. Liability “cannot be transferred to an AI system as it does not have a separate legal personality,” and using client data in a way that could breach confidentiality, including to train AI, “is likely to require specific client consent.” It points to the 2024 Moffatt v. Air Canada ruling, where the airline argued its chatbot was a separate legal entity and lost. Guidance from a representative body, not a regulator, and British, but the reasoning travels to any firm where a professional signs the work. Law Society practice note · Artificial Lawyer
• Nobody to complain to. In an October 19 speech, the UK Financial Conduct Authority’s chief data officer said four in five less-experienced investors aged 18 to 40 have used AI to help them invest, while only 40 percent of consumers recognise there is no recourse when that help is wrong. The FCA is asking AI providers for clear, consistent disclaimers, and the same day published good and poor practice examples for firms. Quoted from the speech as drafted, which the FCA notes may differ from delivery. FCA speech · FCA good and poor practice
• Agents need a name tag. Atlassian co-founder and CEO Mike Cannon-Brookes told Fortune “the accountability is with Mike,” not with the AI, and Atlassian now gives each agent a visible profile and a named owner. The same day Microsoft launched tools that treat agents like insider threats. Both companies sell the tools that answer the problem they describe. Strip the products out and two controls remain: a person’s name next to every agent, and every agent action somewhere a person can read it. Fortune · TechCrunch

The question we asked

If an AI tool in your business gets something wrong tomorrow, whose name is on it? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own hardware, not on a cloud voice service. Every line of the script is reviewed and approved by a person on the Modular team before it airs. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, October 22, 2026: Not a Legal Person appeared first on Modular Technology Group.

Artificial Lawyer ran a piece this week on accountability by design in agentic contract management, and it puts a name on something we keep running into in agent pilot conversations: nobody’s real objection to contract agents is capability. The objection is that when the agent does something wrong, nobody can say who owns it.

That’s the right objection. And it’s fixable, but only if you treat accountability as an architecture decision instead of a policy memo.

Why contracts are the hardest room in the building

Think about what a contract agent actually touches. It reads negotiated terms. It proposes redlines. It extracts obligations and renewal dates and feeds them downstream to finance and ops. In more ambitious deployments it drafts counterparty emails or kicks off approval workflows on its own.

Every one of those actions has a legal consequence attached. A missed auto-renewal clause isn’t a bug ticket, it’s money. A redline that quietly softens an indemnification cap is malpractice territory if a human never saw it. Contracts are where the gap between “the model was 94% accurate” and “we are liable for the 6%” stops being abstract.

So when a GC asks “who is accountable when the agent errs,” the answer cannot be a shrug toward the vendor’s terms of service. The answer has to be visible in the system itself.

What accountability by design looks like when you build it

Here is what we mean when we talk about AgentWorks-style oversight, in concrete terms:

Every action is logged, and the log is legible. Not a JSON dump an engineer can grep. A record a lawyer can read: what the agent did, when, on which document, based on which instruction, with what confidence. If your audit trail requires a data scientist to interpret, you don’t have an audit trail. You have exhaust.

Permissions are scoped per agent, per task. The agent that extracts renewal dates does not get write access to the redlining workflow. The agent that drafts counterparty emails cannot send them. Least privilege isn’t a new idea. Legal teams have run it on humans for a century through signing authority and matter permissions. Agents deserve the same treatment, and honestly, it’s easier to enforce on software than on a partner who’s in a hurry.

Human gates sit at defined thresholds, not everywhere and not nowhere. Blanket “human reviews everything” defeats the point of the agent. Blanket autonomy defeats the point of having a legal department. The useful middle: the agent handles routine NDA turns solo, but anything touching liability caps, IP assignment, or dollar figures above a set line stops and waits for a named person. That threshold is a dial your team sets. It should live in a dashboard, not in a prompt somebody wrote in March and forgot about.

Escalations name a human. When the agent hits its confidence floor or its permission ceiling, the handoff goes to a specific person with a deadline, and the dashboard shows aging escalations the way a docket shows deadlines. Accountability that doesn’t attach to a name isn’t accountability.

You can replay the decision. Six months from now, when a counterparty disputes a term, you need to reconstruct exactly what the agent saw and suggested and what the human approved. Versioned inputs, versioned outputs, versioned instructions. This is the part most pilots skip and most regret.

None of this is exotic. It’s the same discipline legal ops already applies to matter management and billing approval, extended to a new kind of worker.

The part the article gestures at but doesn’t say out loud

There’s a quieter question under all of this: where does the oversight layer itself live?

If your agent runs inside someone else’s cloud, on someone else’s logging, under someone else’s retention policy, then your accountability story has a hole in it shaped exactly like your vendor. You can’t produce audit records the vendor didn’t keep. You can’t enforce a permission boundary the platform doesn’t expose. You can’t guarantee the contract text that flowed through the agent never trained anything, because that guarantee was never yours to make.

Your data, your rules. And in an agentic system, that has to include agency over the AI working on that data. Your AI, your rules. The oversight dashboard, the action logs, the escalation thresholds, all of it should sit on infrastructure you control, governed by policy your team wrote, auditable without asking anyone’s permission. For a legal team this isn’t a philosophical preference. Privilege and confidentiality obligations follow the documents wherever they go. An agent architecture that can’t answer “where did the contract text travel” hasn’t earned the word accountable.

This is where we think agent pilots should start, by the way. Not with the flashiest use case, but with the use case where the oversight scaffolding is easiest to prove out. Run the renewal-date extraction agent first, watch the logs, tune the escalation thresholds, let the team build trust in the dashboard. Then move up to redlining. The scaffolding is the product. The agent is just the first tenant.

The grounded version

The Artificial Lawyer piece is right that accountability has to be designed in, not bolted on. Where I’d push further: designed in means owned. Owned logs, owned permissions, owned thresholds, owned infrastructure. A vendor can sell you an agent. Nobody can sell you accountability. That one you have to hold.

If you’re running or scoping a contract agent pilot right now, I’m genuinely curious: where did you set the line between what the agent does alone and what waits for a human, and how did you pick it? That threshold decision is the whole design in miniature, and I’d like to hear how it’s playing out at your firm.

The post Accountability by design is the only way contract agents get hired appeared first on Modular Technology Group.

The Register ran a piece this week that should get more attention than it will: Salesforce partners are reportedly not seeing meaningful revenue from Agentforce. Not customers grumbling. Partners. The consultancies and integrators who staffed up practices, sat through the certifications, and built their 2026 pipeline around Marc Benioff’s favorite word.

That detail matters more than another “AI pilot stalls” headline would. Partners are the canary in any enterprise software ecosystem. They only make money when customers actually deploy, expand, and renew. When a vendor’s sales numbers look fine but the partner channel is starving, it usually means the product is being sold but not used. Licenses in a drawer. Pilots that never graduate.

I’ve been saying for a while that agents bolted onto a SaaS platform were going to hit this wall, and I want to walk through why, because the reasoning matters more than the schadenfreude.

The bolt-on problem, specifically

An agent is only as useful as what it’s allowed to touch and what it’s trusted to do. Agentforce, by design, lives inside Salesforce. That’s great if your entire operational reality lives inside Salesforce. Almost nobody’s does. The customer record is in the CRM, sure, but the contract is in a document system, the invoice history is in the ERP, the actual conversation with the client is in email, and the tribal knowledge is in somebody’s head or a shared drive from 2019.

So the “agent” either stays narrow enough to be safe and ends up doing work a workflow rule could have done in 2015, or it reaches outward through connectors and suddenly you’re paying platform prices for integration work while your governance story gets murkier with every hop. Neither version produces the outcome the demo promised. Partners can’t bill against outcomes that don’t materialize.

Then there’s pricing. Agentforce launched with per-conversation pricing, and consumption models like that put the customer in a strange position: the more successful the agent is, the bigger the bill, and the bill is set by the same vendor that controls the platform, the model access, and the switching costs. CFOs noticed. Variable AI bills tied to usage you can’t fully predict are exactly the kind of thing that keeps a pilot a pilot forever. Nobody signs off on production when they can’t forecast the line item.

What actually gets agents into production

Here’s what we’ve learned building AgentWorks deployments, and it’s almost boringly unglamorous.

First, the job comes before the agent. You don’t buy an agent platform and then hunt for use cases. You pick one process with a measurable cost, in hours or dollars or errors, and you build the agent to do that process. Scope is a feature. An agent that does one thing inside hard boundaries ships. An agent that “can do anything across your org” gets stuck in security review, and it should.

Second, governance is designed in, not reviewed in. Every AgentWorks agent runs against defined permissions, logged actions, and a human checkpoint wherever the blast radius justifies one. When compliance asks “what can this thing actually do, and who approved it,” the answer is a document, not a shrug. Your data, your rules. And that has to include the AI working on that data. Your AI, your rules, which means you decide what the agent reads, what it writes, which model it runs on, and where all of it lives. On a platform vendor’s agent, most of those decisions were made for you before you ever logged in.

Third, the economics have to be legible. We do fixed pricing on this work because a client cannot govern what they cannot forecast. If the marginal cost of the agent doing its job is a surprise, the agent will be throttled by the finance department long before it’s throttled by any technical limit.

And fourth, model agnosticism. The right model for a contract-summarization agent and the right model for a triage agent are often different, and both will be different again in eight months. An agent architecture that lets you swap the model without rebuilding the system is worth more than any single model choice you make today. Platform agents lock that decision to the platform’s roadmap.

The honest read on the Register story

None of this means Salesforce built something worthless, and this isn’t a pile-on. It means the “add agents to the platform you already pay for” pitch has a structural flaw: the platform’s interests and the customer’s interests diverge exactly at the points that determine whether an agent earns its keep. Scope, data reach, pricing, and model choice. The partners caught in the middle are just the first ones to feel it, because they’re the only party in the ecosystem paid strictly on real adoption.

The agent era is going to be won by narrow, governed, accountable deployments that a specific team relies on every day. We use ours daily, which is how we know which parts of the pitch survive contact with an actual Tuesday.

If you’ve run an agent pilot this year, on Agentforce or anything else, I’m genuinely curious: what killed it or what saved it? Was it governance, cost, scope creep, or something nobody warned you about? Tell me. The failure stories are teaching us more than the keynotes are.

The post When the partners aren’t getting paid, look harder at the product appeared first on Modular Technology Group.

The post DBS gave 1,500 employees AI agents. The interesting part is what had to exist first appeared first on Modular Technology Group.

Three different authorities, in two different countries, inside ten days. A bar association, a securities examiner and a national cyber agency. Not one of them asked whether anybody’s AI was any good. All three asked the harder question: can you show your work?

In this episode

• The default setting is the policy. The New York City Bar Association released Formal Opinion 2026-2 on August 17 (the opinion itself is dated August 5), extending two earlier opinions from clients to co-counsel, opposing counsel, witnesses, prospective clients and a firm’s own investigators. Get consent from every participant before an AI captures a conversation, and absent a good reason, the default should be to leave it switched off. The duty of competence now includes knowing how to turn the thing off. A New York City opinion rather than a national rule, but the reasoning travels. Formal Opinion 2026-2
• What examiners want is the minutes. Financial Advisor Magazine, working from an examination request document obtained by Citywire, reports what SEC examiners are asking investment advisers about AI: does the firm have an AI committee, does it keep written minutes, which channels promote the firm’s AI (the “AI washing” probe, with two 2024 cases settled for a combined $400,000), and were employees ever trained on the policy. No rule requires a document titled “AI policy”; advertising, compliance, privacy, recordkeeping and fiduciary rules already do the work. The ask is evidence, not a binder. Financial Advisor Magazine
• Agent governance, written down at last, and it is four controls. The UK’s National Cyber Security Centre published interim guidance on agentic AI on August 20. Every agent gets its own identity in a class distinct from humans, credentials scoped and short-lived, a named individual accountable for its activity, and its actions logged into monitoring the way a person’s are. Their maturity ladder ends, for the most sensitive work, at no external access with the model hosted locally. Interim guidance from a British agency, not American law. NCSC

The question we asked

Pick the newest AI tool inside your business. Can you name the person accountable for it, and show what it did last week? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, August 22, 2026: Show Your Work appeared first on Modular Technology Group.

Cale is back from Ai4 in Las Vegas, and the news wrote his trip report for him. Three stories this week, and one question underneath all of them: when an agent acts, who answers for it?

In this episode

• A federal court says the human who points the agent is the one who acted. On August 4 the Ninth Circuit vacated the injunction Amazon had won against Perplexity’s Comet shopping agent. Under the federal anti-hacking law, the court read “access” as a person entering a system; software is a tool, and tools do not carry liability. One circuit, an early stage, and Amazon’s other theories are still live. Opinion, No. 26-1444 (PDF)
• “A human reviews it” has a measured failure rate. Belgian developer Alex Wauters built a browser game that flashes real agent permission prompts on a sixty-second clock. Across more than forty thousand rounds, players approved roughly one dangerous request in three. Anthropic’s own telemetry puts real-world approval around ninety-three percent. The Register
• Half a continent could last one business day. A survey of 1,500 firms across the UK, France and Germany found 73.9% worried Washington could cut off their access to US technology, and 54.5% said they could operate for a single business day without their cloud services. 44% have a continuity plan they have tested. The survey was commissioned by Proton, which sells the sovereign alternative; weigh it accordingly. The Register

Report from the floor: Ai4 2026

Cale worked more than thirty booths at The Venetian, August 4 to 6. Three things he brought home.

• The argument won. A main-stage keynote titled “From Renting AI to Owning Intelligence.” Appliance vendors printing “No tokens. No surprises.” on their booth walls. The CIO of a NATO procurement agency describing keeping local models close for sensitive work. Owning your AI has stopped being a niche position.
• Most “agent governance” is a dashboard. It shows you what your agent did. Far fewer products will stop it, and watching an agent misbehave in real time is not a control. The crowd of new names read like the late nineties; Cale’s own bet, offered as a bet, is that many are gone within a year. If your oversight lives in a startup’s dashboard, your controls inherit that startup’s odds.
• Ng and Hinton. They disagree about where this goes, and Hinton’s doom ran a little thick for Cale. The take-home fits in two sentences: build now, the way Ng says. Govern like Hinton is right.

The question we asked

If one of your agents did something wrong yesterday, could you reconstruct what it touched, or would you be guessing? Get the Modular Briefing by email and reply to it. A person reads every reply.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, August 10, 2026: The Agent Was You appeared first on Modular Technology Group.

Agentic AI, agent governance, permissions, ~5 min read

Somebody at your company has already handed an AI agent a credential. Maybe it went through review. Maybe an operations manager wanted invoice reconciliation to stop eating her Fridays, found a tool that promised it, and pasted in a service account key. The agent works. It has been working for six weeks. Nobody wrote down what it is allowed to touch, who owns it, or how you would switch it off at four o’clock on a Friday.

A piece published on BleepingComputer on 29 July puts the mechanism plainly: agents guess. They try an action, read what comes back, adapt, and try again. The article is sponsored content from an identity security vendor, so take the product pitch with the usual salt. The diagnosis holds up anyway, and it is the part worth putting in front of your own team.

Guessing is the job description

A script does the same thing every time. You can read it, test it, and know what it will do in production because it has no capacity to do anything else. An agent works differently on purpose. You give it an objective, and it decides on the path. That flexibility is why anyone wants one. It is also why you cannot write an allow-list of the actions it will take, because neither you nor the vendor knows what they will be until it takes them.

Most of the safety work being sold right now sits downstream of that. Prompt filters, content policies, behavioral monitoring: all of them inspect what the agent is trying to do after it already holds the keys to do it. The BleepingComputer piece has a good line about the arithmetic there. One percent of infinity is still infinity. An agent making thousands of decisions a week against a filter that catches almost everything will still get through, and when it does, the only thing standing between a wrong guess and a bad outcome is what that agent’s credential can reach.

So the useful question is not whether the agent will be wrong. It will be wrong. The question is how far a wrong answer travels.

The math nobody put in the budget

Palo Alto Networks surveyed 2,930 security leaders for its 2026 Identity Security Landscape report and found 109 machine identities for every human identity in the enterprise, up from 82 to 1 the year before. The same research found that 96% of respondents say their human identities already operate with access well beyond what their roles require.

Two more numbers from that report are the ones I would put on a slide. Only 37% of organizations can revoke an AI agent’s credentials. Only 30% keep immutable audit logging of what those agents did.

Sit with that for a second. A third of companies can turn an agent off. Under a third can reconstruct what it touched. Everyone else has deployed something that acts on their data, at speed, with standing access, and has no mechanism to stop it or to explain it afterward. That is not a technology gap. It is a governance gap wearing a technology costume.

Small and mid-sized organizations tend to assume this is an enterprise problem because the identity counts are enterprise counts. The ratio is the point, not the total. A 45-person company running a handful of agents across email, finance, and a shared drive has the same structural exposure as a bank, minus the identity team that would notice.

Scope the mandate, not the model

The fix is old and boring, which is why it works. Treat an agent the way you would treat the person whose work it took over.

That person had a job description. They had a manager. They had access to the systems their job required and not the ones it did not. When they left, IT closed the account, and there was a record of what they had done while they were there. None of that was exotic. It was just applied consistently, because HR and IT had a shared process that made skipping it awkward.

Applied to agents, it comes down to four things you can start this week:

• Find them. Every organization has more agents running than leadership has approved. Ask each department head what is automated and who set it up, and write the answers down. This is a conversation, not a scan.
• Give each one a named human owner and a written mandate. What is this agent for, what data does it need, what is it explicitly not permitted to do. If nobody will put their name on it, that is your answer about whether it should be running.
• Scope the credential to the mandate, and make it revocable. Standing broad access is the multiplier that turns a wrong guess into an incident. An agent that reconciles invoices does not need write access to the CRM.
• Log what it did, somewhere it cannot edit. When an auditor, a client, or your own board asks why a decision came out the way it did, “the AI did it” is not an answer that survives contact with anyone.

Notice that none of this requires you to predict the agent’s behavior. It requires you to bound it. That is the shift the BleepingComputer piece is arguing for, and it is the same shift identity teams made twenty years ago when they stopped trying to trust users and started scoping them.

Your data, your rules, and that includes the AI working on it

Your AI, your rules. That means an agent operating inside your walls, on infrastructure you control, with permissions you set and can withdraw, and a record you own of everything it did. Not a promise from a vendor that their filters are good. A boundary you can point at.

At Modular Technology Group we build agent programs this way because the alternative gets expensive in a specific, predictable direction: the day something goes wrong and nobody can say what happened. Modular’s agent work runs under a written policy from day one, with a named owner per agent, scoped and revocable access, and an audit trail that lives on the client’s own infrastructure rather than a vendor’s.

Agents are worth having. Ours run our own operations daily. But an agent is a delegation of authority, and delegation without a mandate is just hoping. Start with the list of what is already running. Most teams find it is longer than they expected, and that discovery alone is worth the afternoon.

The post Your agents will guess. Permissions decide what that costs you. appeared first on Modular Technology Group.

Three stories this week, and one question underneath all of them: when the AI does something, whose name is on it?

In this episode

• Your team is already using AI outside their job. An OpenAI analysis of more than 800,000 work messages found 44% of occupation-specific messages involved tasks outside the sender’s own role. For HR professionals it was 69%, third highest of any group, and the crossover runs highest at small companies. HR Dive
• What real oversight looks like. The Society of Pension Professionals published a five-principle AI governance framework. Two travel to any industry: tier AI uses by risk instead of writing one blanket rule, and make sure the human reviewing an automated decision has the authority to overturn it. Finextra
• “AI did it” is not a defense. After a rogue agent went after another company’s systems, lawyers were asked who is liable. Nobody knows yet, because the law was built around human decision makers. Liability turns on who designed the system, who set its objectives, what safeguards existed, and how much independence was judged acceptable. The Register

From the week

Cale’s post on a bank hiring its first Chief AI Officer reached more than thirteen thousand people. Two later versions of the same argument reached about two hundred each. The difference was posting the day the news broke and opening with the specifics.

On the road

Modular is at Ai4 in Las Vegas, August 4 to 6, at The Venetian.

The question we asked

At your firm, right now, if somebody asked who approved the AI that touches your client files, is there a name? Or is there a document? Tell us. Get the Modular Briefing by email and reply to it, and it lands with a person who reads it.

A note on the voices

Laura and Arthur are AI-generated voices, produced locally on Modular’s own infrastructure. The reporting, editorial judgement and script are the work of the Modular team. Facts and figures are drawn from the linked sources; check them there before acting on anything.

Transcript

Your data, your rules.

The post The Modular Briefing, July 31, 2026: Is There a Name on It? appeared first on Modular Technology Group.

Episode 8 · 4:40 · Download MP3 · RSS · Transcript

An AI agent is not a feature you switch on. It is an actor with authority. This week: a single link that could build a working agent inside somebody’s workspace with every connector attached and approvals switched off, an attacker who ran an agent unattended inside a national finance ministry, and a government that stopped a billion-euro cloud procurement to ask where its data lives. Three sizes of the same question.

In this episode

• A link that built an agent. Researchers showed one crafted URL could create a working agent inside a logged-in account, attach every connected mailbox and file store, set approvals to never ask, and put it on an hourly schedule. Patched on June 8, with no reported exploitation in the wild. The open question is not the bug. It is how casually agent authority gets handed out.
• An agent nobody was supervising. A security firm reports an attacker running an off-the-shelf agent with its approval mode disabled for post-exploitation work inside a national finance ministry. One firm is the only public source and the ministry has not confirmed it. The transferable lesson is that the useful setting was the one that removed the human.
• A government asking where its data lives. Ireland’s Office of Government Procurement cancelled a cloud framework competition over digital sovereignty. Jurisdiction moved from a slide in a security review to a reason to stop a contract.

Sources

• The Hacker News, July 24, 2026 — agent-builder flaw
• The Hacker News, July 24, 2026 — unattended agent at a national finance ministry
• The Register, July 22, 2026 — Ireland stalls cloud tender over digital sovereignty

AI voice disclosure

Laura and Arthur are AI-generated voices, produced locally on Modular’s own hardware. Story selection, reporting, and fact-checking are done by the Modular Technology Group team. Your data, your rules applies to our own production too.

If your team is pointing AI agents at real systems and nobody can say who owns them or what they may touch, book a conversation.

Your data, your rules.

The post The Modular Briefing, Episode 8: Who Gave It the Keys? appeared first on Modular Technology Group.