Category: Data Engineering & MLOps

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Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments.

Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models to complete multi-step tasks.

Users access this ecosystem through a single OpenAI-compatible endpoint. Fugu routes queries internally, deciding whether to resolve a prompt directly or to assemble a coordinated team of expert models for deeper analysis. The system handles model selection, delegation, verification, and synthesis internally. Engineering teams interact with what appears to be one model while a background system of specialists executes the actual computation.

Sakana AI targets the geopolitical and regulatory risks associated with AI sourcing. Recent export controls affecting Anthropic models like Fable and Mythos demonstrated that access to specific foundational architectures can vanish based on foreign policy decisions.

Fugu functions as a hedge against these sudden supply chain disruptions. The platform relies on a completely swappable agent pool. Fugu dynamically routes traffic around any restricted or degraded provider to maintain service continuity. Sakana AI states this capability provides the resilient architecture required for AI sovereignty.

Fugu deployment tiers

Two tiers are available to accommodate different operational latency requirements.

The standard Fugu model prioritises low latency for daily tasks, integrating into standard developer tools like Codex for live coding and code review. Organisations subject to strict data governance or privacy mandates can manually opt specific underlying models out of the standard Fugu routing pool.

Fugu Ultra targets complex, multi-step analytical problems that demand maximum accuracy. The Ultra variant coordinates a deeper pool of expert agents for intensive tasks such as academic paper reproduction, literature investigations, and patent analysis.

Sakana AI reports that Fugu Ultra performs competitively against leading closed models like Fable 5 and Mythos Preview across scientific, engineering, and reasoning benchmarks:

The orchestration method ensures companies can access top-tier computing capabilities without carrying the vendor concentration risk or export control exposure inherent to those closed models.

Implementation in cybersecurity

Almost 500 early users tested the system during an extended beta program focused on lengthy, multi-step computational workflows. With cybersecurity such a focus for models like Claude Mythos, engineering teams deployed Fugu Ultra to automate complete security assessment cycles.

Human operators issued one scoped instruction, and the orchestration engine executed the entire reconnaissance phase. The model successfully conducted cross-site scripting and SQL injection checks alongside thorough authentication reviews.

A participating cybersecurity engineer confirmed the model stayed strictly within its operational parameters and avoided initiating destructive actions against the target infrastructure. Fugu concluded the automated engagement by generating a clean vulnerability report complete with verifying evidence and exact retest steps for human remediation teams.

The implementation demonstrated that multi-agent routing maintains strict compliance boundaries while executing complex penetration testing sequences.

Software development teams also integrated Fugu Ultra into their primary code review pipelines to compare defect detection rates against established monolithic tools. The orchestration engine consistently outperformed baseline models in identifying logic flaws and security vulnerabilities within complex enterprise codebases.

“For code review, Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss,” reported a software engineer involved in the beta deployment. “Where other tools flag about three issues, Fugu surfaced more than twenty. It’s become the model I run all my reviews through.”

Automated research and persona stability

Data science units deployed the system in an almost fully-automated research mode. Fugu Ultra successfully explored mathematical hypotheses, executed experimental code runs, interpreted failure states, and revised its own approaches to sustain progress over extended periods with minimal human intervention. This capability directly addresses the operational limitations of single-call models that require constant human prompting to recover from logic errors.

Leadership at an unnamed enterprise platform company identified long-term persona stability as a primary advantage during these extended sessions. Conventional monolithic architectures often suffer from context degradation and identity drift when processing extensive conversational histories.

“Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift,” the executive stated. “For agent products, that may matter more than raw benchmark scores.”

Extended benchmark validation

Sakana AI built the internal routing logic upon extensive research into learned model orchestration. The technical foundation for the product stems from findings published in the company’s ICLR 2026 papers, specifically the Trinity and Conductor frameworks.

These academic foundations allow Fugu to process requests by understanding precisely when a task requires delegation versus direct resolution. The internal language model dictates communication protocols between the individual agents and structures the final synthesis of their separate computational outputs.

Validation testing against frontier AI competitors covered complex, open-ended disciplines ranging from financial time series prediction to mechanical design. Fugu also demonstrated high proficiency in niche physical logic tests and visual interpretation tasks, including solving the Rubik’s Cube and performing Japanese handwriting analysis. The capacity to excel in both quantitative financial modelling and qualitative image processing confirms the efficacy of the multi-agent orchestration approach.

Sakana AI designed the system to scale organically as the broader AI hardware and software market matures. Because the product relies entirely on learned orchestration logic rather than fixed operational rulesets, it automatically benefits from third-party innovations. Sakana AI plans to continuously expand the available pool of expert agents.

The engineering team will fold newly-released open-source tools and proprietary Sakana AI models into the routing pool as they become available. Both the standard Fugu and Fugu Ultra models are available to enterprise clients today.

See also: SAP and Google Cloud deploy agentic commerce architecture

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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The post Mitigating vendor lock-in with Sakana AI Fugu multi-agent models appeared first on AI News.

June 22, 2026
SAP and Google Cloud deploy agentic commerce architecture

SAP and Google Cloud are deploying agentic commerce architecture to automate multi-agent marketing and retail operations at enterprise scale.

SAP research indicates 78 percent of businesses consider AI essential for retaining customers in 2026. However, the same data reveals fewer than two in five companies share customer data across customer experience (37%) or CRM (39%) platforms.

Addressing this structural data failure requires direct infrastructure intervention. SAP and Google Cloud expanded their partnership to build an agentic customer experience architecture, connecting data, AI, engagement, and commerce operations.

The deployment relies on restructuring how AI interacts with backend commercial platforms. Most digital commerce infrastructures rely on fragmented APIs. SAP Commerce Cloud adopts the Universal Commerce Protocol to standardise data exchange among retailers, payment gateways, and autonomous agents. This framework allows software to independently execute the full retail sequence, spanning initial search, transaction processing, and post-sale resolution.

Deploying the Universal Commerce Protocol

Engineering teams integrating the Universal Commerce Protocol facilitate direct interactions between intelligent agents and commerce platforms. The standardisation lowers integration costs and accelerates onboarding into AI-driven channels.

SAP plans to collaborate with Google to ensure merchant products surface organically across the Gemini application and Google Search, specifically incorporating AI Mode functionalities. Consumers interact with these interfaces while the backend architecture processes inventory checks, cart management, and payment processing without requiring retailers to rebuild existing infrastructure.

SAP Commerce Cloud integrates Google Gemini capabilities to power a designated Shopping Assistant. Brands deploy the assistant directly to their consumers to facilitate chat, voice, and text engagements. State retention remains active throughout the complete shopping cycle. The deployment ingests live behavioural inputs, current warehouse capacities, and active marketing data to assemble distinct merchandise pairings, including full event configurations. By continuously refining recommendations, the application ensures high relevance and strict physical fulfilment capability.

Enterprise systems often fail when promotional campaigns trigger demand that physical inventory cannot satisfy. Frontend interfaces failing to synchronise with backend warehouse systems frequently halt digital purchases. Users regularly click promotional emails, load the associated mobile application, and face sudden out-of-stock notices during checkout. Fulfilment updates experience severe delays, leaving support agents without a complete operational picture. SAP and Google Cloud engineered their joint solution to correct these specific systemic customer experience failures.

Instead of managing disconnected points of contact, the architecture unifies the entire sequence. Traditional commercial setups require consumers to repeatedly input previously shared information. Support staff frequently lack access to unified records, preventing them from resolving issues efficiently. The integration targets these operational breakdowns, ensuring the system recognises the user and their precise context instantly across all digital properties.

Bidirectional data flows

Marketing execution demands highly accurate data pipelines. SAP Engagement Cloud partners with Google Cloud to formulate an autonomous multi-agent framework. The technical foundation relies on SAP Business Data Cloud Connect for Google BigQuery. The deployment relies on bidirectional, zero-copy data linking secured by strict administrative controls. Leaving vast data stores in place rather than duplicating them drops storage expenses and network latency.

BigQuery ingests live variables like weather conditions, precise locations, and active advertising interaction rates. SAP Customer Experience solutions supply the internal behavioural context, tracking customer profiles, exact transaction histories, specific service interactions, and consented engagement records. SAP Engagement Cloud activates the combined intelligence, deploying autonomous agents to orchestrate personalised interactions throughout the customer lifecycle.

Routing information through the Business Data Cloud while BigQuery handles the logic forces immediate inventory synchronisation. The Shopping Assistant actively queries live warehouse records before displaying any product. Software checks physical supply against consumer requests, verifying availability prior to making the suggestion.

Generative execution in production environments

Advanced generative models dictate the localised output of the marketing campaigns. Google Gemini models, specifically including the Nano Banana 2 iteration, provide specialised agentic skills. The models dynamically generate localised messaging, customised imagery, and campaign variations based on the exact specifications provided by the bidirectional data flow.

The deployment upgrades standard text messages into immersive and interactive interfaces via Google Rich Communication Services. Advertising creatives evolve continuously based on incoming engagement data. The system processes the interaction, evaluates the response against the user profile, and instructs the Nano Banana 2 model to adjust the subsequent communication.

Marketing departments achieve high efficiency by abandoning manual execution. Instead of configuring rigid campaign parameters, teams establish business goals and provide enterprise data access to the SAP Engagement Cloud. The autonomous agents coordinate the necessary steps, segmenting audiences based on Google BigQuery analytics and generating specific content variations through Google Gemini models.

Evaluating the infrastructure impact

Deploying the architecture restructures standard commerce operations. Consumers dictate their purchasing intent to search engines and conversational interfaces. The embedded AI agents process the intent, navigate the Universal Commerce Protocol connections, and complete the purchase directly against the enterprise backend.

Retailers retain full ownership of the customer relationship despite the transaction occurring within a third-party environment. The architecture captures the consented engagement data, feeding the transaction history back into the SAP Customer Experience solutions. The system updates the localised customer profile, providing the Google Gemini models with fresh context prior to the next engagement cycle.

The system continuously improves campaign performance without requiring direct human intervention. The multi-agent framework evaluates the success of a generated Rich Communication Services text message, adjusting the variables prior to the next automated dispatch.

See also: Computer vision deployments drive retail productivity gains

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post SAP and Google Cloud deploy agentic commerce architecture appeared first on AI News.

June 19, 2026
Google Cloud generative AI automates council planning operations
Government ministries are deploying Google Cloud generative AI across municipal agencies to automate council planning operations.

Public sector administration handles vast volumes of unstructured data that delay infrastructure development. The UK central government established a target to construct 1.5 million new homes by 2029. Local planning authorities encounter administrative backlogs caused by dense paperwork, delaying these development timelines.

To address these constraints, the Ministry of Housing, Communities and Local Government (MHCLG) and the Department for Science, Innovation and Technology (DSIT) expanded two machine learning tools designed to accelerate municipal processing. Speaking at the Google Cloud Summit London, officials confirmed the nationwide deployment of the ‘Extract’ application and the progression of the ‘Augmented Planning Decisions’ (APD) prototype.

Lila Ibrahim, Chief AI Readiness Officer at Google DeepMind, said: “The UK has an opportunity to build the homes our communities need, but local councils face a mountain of paperwork. That’s why we’re co-creating a sophisticated planning tool directly with councils to solve real-world bottlenecks.

“This will help significantly cut decision times, freeing up planners to focus on the future to get Britain building faster.”

Householder applications – which include routine domestic modifications such as loft conversions or property extensions – account for nearly 70 percent of all planning applications submitted annually. Evaluating these standard submissions manually requires planning officers to spend hours cross-referencing regional policy documents, historical archives, and unstructured PDF files.

Such a repetitive evaluation process consumes administrative hours that would otherwise support major infrastructure and commercial developments. The deployment of automation targets this administrative distribution, aiming to reduce application decision timelines by 50 percent.

Core capabilities of the Google Cloud generative AI tools

Engineers at MHCLG and the government’s applied AI team, the Incubator for AI (i.AI), built the Extract tool internally using Gemini foundation models. Following trials across more than 20 local planning authorities, administrators expanded the application to every council in England.

Extract parses unstructured data locked within legacy PDF records, converting hundreds of pages of historical planning documentation into structured digital datasets within minutes. Operational data from the trial phases indicates that the tool will eliminate roughly 255 hours of manual data entry per council annually. This reduction allows local authorities to reallocate personnel to complex evaluation tasks.

Integrating large language models into public sector workflows requires enterprise-grade security environments. Local authorities process sensitive civic records, requiring strict risk management protocols to prevent data exposure.

The government hosted the Gemini models on Google Cloud to establish a protected operating environment where data sovereignty is maintained. The cloud environment features active security controls to block malicious inputs, including prompt injection attacks. This technical framework ensures that sensitive municipal data remains secure during both testing and production computing cycles.

The APD system, meanwhile, acts as an analytical assistant for municipal planning officers by automating four primary administrative tasks:
1. The system consolidates incoming documentation by pre-processing data backlogs, flagging missing information gaps, and extracting core geographical site data onto a unified user interface for officer review.
2. The software identifies relevant national and local zoning laws, assesses compliance margins, and appends precise policy citations for manual verification.
3. The application parses public consultation letters, summarising stakeholder objections or historical legal precedents.
4. The model generates initial drafts of final evaluation reports, including the technical rationale and recommended approval conditions.
Protocols dictate that human planning officers retain final decision-making authority over every application. The software does not automate final approvals or rejections independently. Staff members review every line of text generated by the machine learning models, modifying the analytical reasoning before validating the report.

To maintain regulatory accountability, the APD prototype records its internal processing steps sequentially. This mechanism establishes an auditable chain of thought, creating a verification trail for every processed application to support the officer’s final determination.

Local council planning trials and scaling timelines

The development of the APD prototype relies on a collaborative framework linking public sector administrators with engineering teams from Google Cloud, Google DeepMind, and Faculty.

The alpha version undergoes live testing within three local authorities: the London Borough of Barnet, Dorset Council, and the London Borough of Camden. Testing across these distinct regional jurisdictions provides developers with varied municipal datasets to test the software against diverse local policies.

Central planners intend to complete the alpha phase and deploy the APD tool to all 300-plus English local authorities by 2027. Google Cloud provides the elastic computing infrastructure required to manage the thousands of concurrent inferencing queries generated during daily operations.

Paul Maltby, Director of Public Services at Faculty, commented: “The English planning system is clogged up. Planning officers are forced to spend half their time reviewing applications to convert an attic, putting those for housing estates and warehouses on hold.

“Built with planning officers, our AI system will take the drudgery out of reviewing simple planning applications so they can make quick decisions. It will let planning officers focus on the major developments that matter, and crucially, let families improve their homes without months of delay and uncertainty.”

Naisha Polaine, Executive Director for Growth at Barnet Council, added: “The tool’s ability to collect relevant information, undertake a provisional assessment, and draft the foundations of a report has the potential to save significant officer time spent working on the administration of planning applications and direct this to speeding up the decision-making process for residents. In turn, this will contribute significantly to delivering our house building growth targets in the borough.”

The coordination between MHCLG, i.AI, Google DeepMind, and Faculty establishes a structured division of labour for enterprise software engineering. Public ministries define the policy guidelines and statutory boundaries, while external technical partners engineer and deploy the underlying model architectures.

The successful integration of these systems demonstrates the feasibility of hosting advanced language models within a secured public cloud infrastructure to process core administrative workloads and modernise public service delivery.

See also: EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Google Cloud generative AI automates council planning operations appeared first on AI News.
June 17, 2026