ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

Back to Blog

Super Intelligence Force: What Changes for Agencies Buying AI

ibl.ai EngineeringOctober 9, 2026
Premium

President Trump announced the Super Intelligence Force on 4 October 2026, a task force chaired by DNI Jay Clayton with 120 days to report on AI's risks and opportunities. Its reported charter covers AI threats and recommendations, not contracting; agencies still buy AI chiefly under OMB M-25-22, whose terms already reward data rights, portability and code an agency can keep.

The Short Answer

The Super Intelligence Force, announced on 4 October 2026 and chaired by DNI Jay Clayton, is a coordinating task force with a 120-day report, not a procurement authority. Agencies still buy AI under OMB M-25-22, which already demands data rights, portability and lock-in protection. ibl.ai maps to those terms: you own all the code and the data, model-agnostic, deployed inside your perimeter.

The announcement is real and the people on it are senior. What it is not, on the record so far, is a new body with power over what an agency buys. That distinction decides what a contracting officer should do this quarter.

What is the Super Intelligence Force, and who leads it?

The Super Intelligence Force is a White House task force on AI that President Trump announced in a Truth Social post on Sunday 4 October 2026, as NBC News and TechCrunch reported.

The post said the Force "is tasked with coordinating the effort of the Federal Government to ensure that America continues to lead the World in Super Intelligence."

Four officials lead it, and they report to the President and White House Chief of Staff Susie Wiles:

  • Jay Clayton, Director of National Intelligence, as chair
  • Andrew Ferguson, Chairman of the Federal Trade Commission
  • Emil Michael, Under Secretary of War for Research and Engineering
  • Scott Kupor, Director of the Office of Personnel Management

Per NBC, the Force will coordinate federal engagement with consumers, public interest groups, religious organizations, critical infrastructure providers and AI companies. Clayton previously chaired the SEC, Nextgov reports.

It arrived five days after the White House Accord on Super Intelligence, the voluntary safety agreement six AI and technology leaders signed on 29 September. The accord is a voluntary commitment by companies; the Force organizes government.

Does the Super Intelligence Force have operational or procurement authority?

It has a broad mandate on AI threats, but nothing reported so far gives the Super Intelligence Force authority over what agencies buy. The circulating "operational authority" claim overstates what the charter is reported to say.

POLITICO obtained the charter on 6 October and describes "broad authority to determine how the tech industry and the federal government should address threats" to national security and civil liberties from AI. The Wall Street Journal first reported its details.

Per POLITICO, the charter tasks the Force with identifying "measures to improve the preparedness and response capability of both the government and the private sector to threats to the homeland and the American people."

It also asks for a report on AI risks covering civil liberties, cybersecurity, standards for advanced models and the impacts of possible overregulation. The Force stands for 120 days, and its work can be extended, or its functions transferred to an agency, at Wiles' direction.

It works with the Pentagon, the Justice Department, Homeland Security and the FTC to outline recommendations. Crowell & Moring likewise describes an interagency body that coordinates and reports within 120 days.

None of these sources describes power to award, cancel or set the terms of agency contracts. The charter was obtained by reporters rather than published, and we found no order establishing the Force on whitehouse.gov as of 9 October.

What did the September 2026 executive orders change for federal AI buyers?

Two executive orders signed on 29 September 2026 sit beside the Super Intelligence Force, and neither rewrites acquisition rules.

Executive Order 14434 directs the executive branch to say "Super Intelligence" and "SI" in place of "Artificial Intelligence" and "AI." It is terminology, and it says it does not require altering previously issued contracts.

Executive Order 14432 creates America.gov, a single front door for public-facing services that serve more than 100,000 users a year online, with an OMB implementation memo due within 90 days.

It also commits to "preserve each agency's custody and control of its records, systems, statutory responsibilities, and adjudicatory authority." That is the clause an agency integrating AI into a covered service should hold its vendors to.

On personal data, the order asks for data minimization, secure authentication, auditable authorization and disclosure practices consistent with applicable law.

Which federal rules govern how agencies buy AI right now?

The rules a federal agency buys AI under today are in OMB Memorandum M-25-22, Driving Efficient Acquisition of Artificial Intelligence in Government, signed on 3 April 2025.

It applies to contracts awarded under solicitations issued on or after 180 days after issuance, plus options and extensions after that date. Its companion, M-25-21, sets the minimum practices for high-impact AI uses.

Since December 2025, M-26-04 adds terms for any LLM solicitation: compliance with two Unbiased AI Principles, truth-seeking and ideological neutrality.

Five of its provisions do most of the work for a buyer worried about lock-in:

M-25-22 provision What it requires What to ask a vendor
Use of government data Contracts permanently prohibit training publicly or commercially available AI on non-public agency inputs and outputs, absent explicit consent Where do prompts and outputs go, and who can read them?
Vendor lock-in protections Knowledge transfer, data and model portability, rights to code and models produced under the contract What do we hold on the day we stop paying you?
Pricing transparency Clear licensing terms and transparent pricing in solicitations Does cost track use, or headcount?
Ongoing testing The agency can monitor performance and risk, using test data it defines Can we run our own evaluation set against any model?
Contract closeout A plan to transfer data and derived assets when a contract is not extended Is the exit written down before the entrance?

The pricing row is where per-seat licensing is the wrong shape. A per-seat bill scales with headcount whether or not anyone uses the service, so the price tells an agency nothing about the AI it actually consumed.

Does OMB's AI acquisition memo cover the intelligence community the Force's chair leads?

No. M-25-22 states that its requirements "do not apply to elements of the Intelligence Community," as defined in 50 U.S.C. Β§ 3003.

That makes the chair's position unusual. The Super Intelligence Force is led by the official who heads the intelligence community, a part of government the main civilian AI acquisition memo does not reach.

Two readings are possible, and the 120-day report is where to look for which one holds. Either the Force's recommendations stay at the level of policy and coordination, or they start to shape acquisition practice across agencies with different rulebooks.

Until then, a civilian agency's AI contract is governed by M-25-22 and its own acquisition regulations. The DNI's role on the Force does not change that by itself.

AI accountability in intelligence work is already a live question: in September three senators wrote to the DNI and the Defense Secretary about AI-generated intelligence reports.

What should an agency buying AI do before the Super Intelligence Force reports?

Keep buying under the rules in force, and buy the shape that survives whatever the Force recommends. Counted from the 4 October announcement, the 120-day window runs into early February 2027.

Four steps hold up under any plausible outcome:

  1. Write M-25-22's lock-in terms into every AI solicitation. Knowledge transfer, data and model portability, and rights to code produced under the contract are already policy.
  2. Separate the platform from the model. A recommendation that favours or restricts a model family should mean a configuration change, not a re-procurement.
  3. Keep the audit trail inside the agency. Logs of every agent action and query belong in systems the agency administers, consistent with EO 14432's custody-and-control language.
  4. Price by use, not by seat. Pricing transparency is easier to show when cost follows consumption.

The same reasoning applied to agencies more broadly is in Sovereign AI for Government Agencies, and the case for open-weight models in federal use is in How Washington Made Sovereign AI the Path of Least Resistance.

Where does ibl.ai fit for a federal agency buying AI?

On ibl.ai you own all the code and the data. The platform ships as source code under a perpetual licence and runs inside the agency's perimeter, model-agnostic across any LLM, with no per-seat pricing.

That maps onto M-25-22 directly: the agency holds the code and the data, can move between models without rewriting the platform, and can show what it consumed. It is also the deployment shape that holds if the Force's report tightens the rules.

ibl.ai deploys in your own cloud, on-premise, in GovCloud, or fully air-gapped, with Agentic OS as the platform. 1.6M+ users across 400+ organizations run it this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY β€” a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

Want an AI deployment your agency keeps?

We deploy the platform as source code your agency keeps, under the acquisition terms you already have to write. Book a 30-minute demo or talk to the ibl.ai team.

Sources: the Force's announcement, membership and reporting line from NBC News and TechCrunch; the charter's contents from POLITICO, which obtained it, and TechCrunch's reporting of the Wall Street Journal; Clayton's background from Nextgov; the Force's coordinating mandate from Crowell & Moring; executive orders 14434 and 14432 and OMB memoranda M-25-22 and M-26-04 from their published text.

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing β€” so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform β€” the stack itself is yours.

  • Model-agnostic

    Run any LLM β€” Claude, GPT, Gemini, Llama, Command, or your own fine-tune β€” and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY β€” a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

Related Articles

Why You Need to Own Your AI Codebase: Eliminating Vendor Lock-In with ibl.ai

Ninety-four percent of IT leaders fear AI vendor lock-in. This article explains why owning your AI codebase -- the approach ibl.ai offers -- eliminates that risk entirely: full source code, deploy anywhere, any model, no telemetry, no dependency. Your code, your data, your infrastructure.

Higher EducationMarch 8, 2026

Six CEOs Signed the White House Accord on Super Intelligence. It Appoints No Regulator.

Six AI and technology leaders signed the White House Accord on Super Intelligence on 29 September 2026. It urges four sets of safety practices, including an independent external auditor and a board-level oversight committee, and it creates no regulator and no penalties. Each signatory appoints its own auditor, which means the only oversight a buyer can enforce is the oversight running inside its own infrastructure.

ibl.ai EngineeringOctober 1, 2026

Sovereign AI: 67 Countries In, Firms Stalled

The CNAS Sovereign AI Index counts 184 government-backed projects across 67 countries in the first half of 2026, most of them infrastructure. Enterprises say 99% are deploying agents and roughly 9-14% have. Governments are building the layer enterprises keep renting.

Mikel AmigotAugust 31, 2026

Sovereign AI Is Now Procurement Policy, Not Rhetoric

France's Ministry of the Armed Forces signed a framework agreement with Mistral in January 2026, and Nigeria's National Digital Cloud Policy scopes sovereignty to government and regulated data. Sovereign AI has moved from speeches into contracts β€” and the contract terms are where it succeeds or fails.

Jaione AmigotAugust 24, 2026

See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work β€” so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope Β· fixed timeline

A time-boxed proof of value on your real data β€” not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time Β· not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data Β· run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Custom quote

perpetual license Β· you own the stack

We transfer the full source code. You own and self-host the entire platform β€” outright.

Best for: Organizations and enterprises that benefit from perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable Β· zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM β€” Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY