Executive summary:
A system of systems approach coordinates independent, specialized components into flexible networks that accomplish what no single system could achieve alone. Each system is an aggregation of smaller systems and a component of a larger system. Such configurations produce force-multiplying capabilities through intelligent coordination.
Monolithic AI systems are too rigid, opaque, and limited for complex, dynamic environments, especially when the stakes are high. Organizations need to field AI that can be configured to spec—while maintaining security, adaptability, and explainability.
Cognitive Hive AI (CHAI) is a system of systems approach to AI deployment that aligns with the Department of Defense's Modular Open Systems Approach (MOSA). Like a colony of specialized bees working in concert, CHAI breaks down complex AI capabilities into discrete, interoperable modules. This modularity enables:
CHAI's architecture is particularly valuable within domains where traditional "black box" AI won't suffice—from defense applications requiring air-gapped deployment to healthcare scenarios demanding clear audit trails. Rather than forcing organizations to accept the limitations of monolithic AI, CHAI enables custom configurations that precisely match operational needs.
If you’d like to explore a CHAI implementation for your organization, let’s talk.
Many organizations approach AI implementation with a flawed assumption: somewhere out there exists the perfect product that will solve their needs. This mindset makes sense for simple applications. If you need a chatbot for basic customer service, plenty of off-the-shelf solutions exist. But for complex, high-stakes applications, searching for the "right product" is like trying to find a Swiss Army knife that can build a house.
For complex use cases, you can’t just “buy one big system” that fulfills all the requirements, says Sae Schatz in her Feb 8, 2024 appearance on the Warfighter Podcast. “Instead, we need to focus on these open data standards and APIs…so heterogeneous technologies can plug together.”
Using a Lego analogy, Sae advocates for “a modular open systems approach, where we make each Lego block within the larger system its own standalone device.”
For example, let’s say that a government agency needs AI capabilities for gray zone threat detection. They may need some or all of the following parameters and capabilities:
No single AI product can deliver this combination of capabilities. Even if it did, the parameters of the project might change, necessitating new capabilities. The complexity, agility, and specialization required make a monolithic solution impractical. More importantly, the stakes involved demand a more thoughtful approach than simply deploying a pre-built system and hoping it works.
The same applies across other high-stakes domains. Healthcare organizations implementing AI for diagnostic support, financial institutions deploying AI for fraud detection, or utilities using AI to manage critical infrastructure—all face similar complexity. Their requirements span security, compliance, integration, explainability, and adaptability in ways no single product can address.
This is where system of systems thinking fundamentally changes the game. Rather than searching for an impossible perfect product, organizations can assemble specialized AI modules into flexible, evolving networks that precisely match their needs. Each module handles specific tasks while coordinating with others through defined interfaces. The result isn't a product but a living architecture that grows and adapts with the organization's needs.
A system of systems treats complex challenges not as problems to be solved by a single solution, but as networks of interrelated tasks that demand coordinated responses. Each component system maintains operational independence while contributing to broader capabilities that no single system could achieve alone.
Think of how a modern military operates. No single weapons platform can handle every threat. Instead, different specialized units—air defense, electronic warfare, infantry, and logistics—work together as independent yet coordinated systems. Each unit maintains its core capabilities while interfacing with others to create effects far beyond what any single unit could achieve.
This approach is transformative for AI deployment. Rather than building monolithic AI systems that do everything, organizations can deploy networks of specialized AI modules that:
A system of systems is very different from a large, complex system.
A system of systems (SoS) maintains independence of its component systems while enabling them to work together toward larger goals. The key difference lies in autonomy and emergence: each component system in an SoS can operate independently and often serves multiple purposes beyond the SoS.
For example, an aircraft carrier battle group is a system of systems—each ship, aircraft, and submarine can operate independently and serve other missions, even if separated from the group. Together they create capabilities far beyond what any single vessel could achieve, but crucially, no single point of failure renders the other components useless.
In contrast, a complex system (e.g., an aircraft engine), while sophisticated, has components that only function within that unified system. If the engine fails, its individual parts—no matter how well-engineered—become effectively worthless since they can't operate independently.
The distinction matters because SoS architectures enable rapid adaptation (you can swap in new capabilities) and inherent resilience (components maintain value and functionality even if isolated from the larger system).
Cognitive Hive AI puts system of systems thinking into practice through a modular architecture that mirrors how honeybee colonies operate. Individual modules (LLMs, data processing, LQMs, or other types of machine learning) take on agentic roles within a CHAI ensemble to create emergent abilities. Then, an entire CHAI ensemble be one module within a much larger CHAI ensemble to tackle increasingly complex challenges.
At the base level, a CHAI implementation might coordinate specialized modules, including:
A CHAI ensemble focused on maritime domain awareness might combine:
While simultaneously, another ensemble handles land-based intelligence, including:
These specialized ensembles can then operate as modules within an even larger CHAI architecture focused on comprehensive threat detection. The possibilities scale up exponentially—each level maintaining its independence while contributing to increasingly sophisticated capabilities. The result is a living, learning system of systems that can adapt and evolve at any scale.
The power of nested CHAI architectures becomes clear when we examine how they transform multi-domain military operations. Consider the challenge of detecting gray zone warfare activities, the ambiguous actions adversaries use to achieve strategic objectives while avoiding conventional military response.
At the foundational level, CHAI ensembles monitor distinct aspects of potential gray zone activity. One ensemble focuses on economic indicators, processing streams of market data, tracking strategic investments, monitoring supply chain vulnerabilities, and identifying suspicious patterns in cryptocurrency flows. Another ensemble concentrates on the cyber domain, correlating network intrusions, infrastructure probes, and potential supply chain compromises. A third watches the information space, analyzing social media manipulation, tracking narrative spread, and identifying coordinated influence campaigns.
A parent CHAI ensemble coordinates all of these. This meta-ensemble correlates patterns across domains, identifying coordinated campaigns that would be invisible when viewing any single domain in isolation. For example, it might detect how a seemingly routine cyber intrusion aligns with specific market manipulations and targeted disinformation campaigns—revealing a gray zone operation that no single-domain system could spot.
At the strategic level, another CHAI ensemble might ingest these cross-domain insights to model potential responses and war-game outcomes and coordinate deterrence actions. The result is a living system of systems that can adapt quicker than our adversaries while maintaining the explainability and accountability that military operations demand.
The same principles that make nested CHAI architectures powerful for defense apply to any complex domain where monitoring individual indicators isn't enough—different systems interact and influence each other.
Take healthcare system management. Individual CHAI ensembles might monitor specific facilities, tracking patient flow, resource utilization, staff scheduling, and clinical outcomes. These facility-level systems feed into regional ensembles that coordinate resource sharing, predict demand surges, and optimize care delivery across networks of hospitals. At the highest level, another CHAI ensemble uses these insights to guide policy decisions, allocate resources, and respond to emerging public health threats.
Or consider financial market oversight. Base-level CHAI ensembles monitor specific market segments, analyzing trading patterns, tracking institutional behaviors, and flagging potential irregularities. These feed into broader ensembles that spot cross-market manipulation attempts and systemic risks. A top-level ensemble then coordinates regulatory responses, adapts monitoring strategies, and helps maintain market stability.
But perhaps the most compelling application lies in critical infrastructure protection. Individual CHAI ensembles can monitor specific infrastructure sectors—power grids, telecommunications, water systems, and transportation networks. These sector-specific systems join in regional ensembles that coordinate infrastructure resilience across domains. At the national level, another ensemble maintains strategic awareness of infrastructure vulnerabilities, coordinates protective measures, and orchestrates responses to cascading disruptions.
In each case, the nested architecture enables clear accountability for every insight and decision. When anomalies emerge, analysts can trace exactly how different levels of the system contributed to the outcome.
CHAI's modular design breaks from traditional AI approaches. Rather than building ever-larger models that try to understand everything at once, or relying solely on language models with bolt-on features, CHAI deploys numerous specialized AI modules that excel at specific tasks while communicating through standardized interfaces.
Think of each CHAI ensemble as a cognitive beehive. The queen bee coordinator—an AI orchestration module—directs an array of specialized worker modules that might include any of the following.
Intelligence gathering:
Processing and analysis:
Integration and response:
Unlike a language model that might excel at text processing but stumbles with numerical analysis, or an image recognition system that can't handle temporal data, CHAI ensembles combine specialized capabilities into coordinated systems that handle real-world complexity. Each module maintains independence while contributing to collective capabilities far beyond what any single AI system could achieve.
CHAI modules can challenge each other. Like a team of experts who collaborate but also play devil's advocate, modules can operate in collaborative or adversarial modes while serving a common goal.
For example, in a threat detection ensemble:
This multi-layered interaction creates more robust outcomes than simple cooperation. When one module spots a potential cyber threat, others might:
Higher-level modules coordinate these interactions, bringing in human experts when needed while maintaining clear audit trails of the entire decision process. This flexibility—modules that can collaborate, compete, verify, or challenge each other—enables sophisticated analytical capabilities that mirror how human teams tackle complex problems.
The magic happens in how CHAI ensembles can stack together. Each ensemble connects through a standardized interface that allows it to function as a module in a larger system. This interface defines how modules communicate, maintain security boundaries, and contribute to clear decision trails.
CHAI's MOSA-compliant architecture enables rapid integration of new capabilities without disrupting existing operations. Need quantum computing modules for cryptography? Want to add new sensor types? As long as they conform to interface standards, they plug right in. The architecture can grow more capable over time while becoming easier to manage.
Think of how the brain processes a simple task such as catching a baseball. Your visual system tracks the ball's motion, while other neural systems calculate trajectory, coordinate muscle movements, and adjust for wind conditions. CHAI ensembles work similarly.
Let's examine some real-world CHAI configurations.
A logistics company deploys a CHAI ensemble that combines satellite imagery analysis of ports and shipping lanes, IoT sensors tracking container movements, machine learning models processing customs data, and specialized language models monitoring global news and social media.
A dynamic knowledge graph module maintains a real-time understanding of the supply chain network, while predictive models forecast potential disruptions. When anomalies emerge—such as a subtle pattern of delayed shipments coinciding with unusual financial transactions—higher-level ensembles correlate these signals to identify potential threats.
Base-level modules process different types of patient data—lab results, imaging studies, genetic profiles, and electronic health records. Specialized machine learning models trained on specific conditions analyze each data type, while knowledge graph modules map relationships between symptoms, conditions, and treatments.
Higher-level ensembles correlate insights across data types, comparing patterns against vast medical databases while maintaining clear reasoning trails for every suggestion. The system doesn't just say "Patient X might have condition Y"—it shows exactly how it reached that conclusion.
At the lowest level, specialized modules monitor specific systems—power grid sensors, network traffic analyzers, and industrial control system monitors. Machine learning models trained on normal operation patterns detect subtle anomalies, while specialized language models process maintenance logs and incident reports.
Higher-level ensembles correlate these inputs to identify coordinated attacks that might target multiple infrastructure types simultaneously. The system maintains the independence of individual monitoring capabilities while enabling rapid, coordinated responses to emerging threats.
AI must evolve rapidly to counter emerging threats, but high-stakes applications demand rock-solid security and crystal-clear accountability. CHAI solves this challenge through secure adaptability zones—a framework that enables rapid evolution while maintaining strict control.
Think of it like a classified military facility. The most sensitive operations happen in secure, air-gapped environments. Other functions occur in less restricted areas but with strict protocols governing access and information flow. CHAI implements this concept through three distinct zones.
Critical AI modules operate in completely isolated environments—physically air-gapped systems processing classified data or logically isolated modules handling sensitive financial transactions. Each module maintains its own security perimeter while following strict protocols for data handling and decision logging.
New capabilities are developed and tested in separate environments that mirror production systems. When modules prove their reliability and security, they can be promoted to the core security zone through rigorous change management procedures. This allows organizations to evolve their capabilities without risking core operations.
Standardized interfaces govern how modules communicate across security boundaries. Like carefully controlled border crossings, these interfaces enable necessary information flows while maintaining security perimeters. Every data transfer, every insight shared, and every decision made is logged and traceable.
Here’s an example of how CHAI can evolve dynamically, taken from the arena of gray zone defense: A CHAI ensemble that monitors social media for signs of foreign influence operations.
When adversaries shift tactics—perhaps moving from obvious bot networks to subtle manipulation of authentic accounts—the ensemble adapts in real time. Pattern recognition modules update their baselines in the adaptation zone, while language models fine-tune to emerging narratives. Most importantly, new specialized modules can be rapidly deployed to counter novel techniques.
This same principle scales up through nested architectures. Consider a CHAI system protecting critical infrastructure:
At each level, adaptations are logged, verified, and coordinated through standardized interfaces. New capabilities can be tested in isolated environments before deployment while existing modules continue their critical work uninterrupted. This architecture solves a fundamental challenge in high-stakes AI: enabling rapid evolution while maintaining absolute accountability. When a system protecting nuclear facilities or managing military responses needs to adapt, there's no room for black-box decision-making.
At Talbot West, we guide organizations through CHAI implementation using a proven, stepwise approach. Rather than pushing for massive system overhauls, we help you build value incrementally while laying the groundwork for broader capabilities.
We start by deeply understanding your operational environment, security requirements, and strategic objectives. Our feasibility studies uncover:
We identify your ideal entry point into CHAI architecture—one that delivers immediate value while establishing patterns for future growth.
Based on the feasibility study, we design and deploy a focused pilot that proves CHAI's value in your environment.
This involves:
We've found that starting with a contained pilot builds confidence while validating the broader CHAI approach.
As your pilot demonstrates success, we help you expand capabilities methodically:
Throughout this process, we maintain strict security boundaries and clear accountability trails. Every addition, every update, every new capability is thoroughly tested before deployment.
The future belongs to organizations that master systems thinking. As artificial intelligence reshapes every sector of society, the difference between success and failure increasingly hinges on our ability to coordinate specialized capabilities into adaptive, intelligent networks.
This shift mirrors broader trends across technology and defense. The Department of Defense mandates MOSA because modern warfare demands rapid capability deployment across domains. The cybersecurity sector embraces zero-trust architectures because perimeter defense alone can't stop sophisticated threats. Critical infrastructure operators build resilience through distributed, interconnected systems rather than centralized control.
CHAI brings this same evolutionary leap to AI deployment. By breaking free from the limitations of monolithic systems, organizations gain more than just technical capabilities—they gain the adaptability to thrive in an increasingly complex world.
Whether defending against gray zone threats, managing critical infrastructure, or protecting financial systems, the challenges ahead demand more than just powerful AI. They demand intelligent orchestration of specialized capabilities. They demand clear accountability for every decision. They demand the ability to evolve rapidly while maintaining absolute security.
They demand, in other words, a system of systems approach.
Let's discuss how CHAI can transform your AI capabilities.
Traditional approaches—building bigger, more complex single systems—can't keep pace with rapidly evolving threats and opportunities. When China develops military capabilities five to six times faster than the U.S., or when gray zone warfare continuously shifts tactics, we need the agility that only a system of systems approach can provide.
Upfront cost of a system of systems (CHAI) AI architecture can be more than a single system or stand-alone solution. But the long-term costs are often lower because you can update individual components rather than replacing entire systems. Plus, you gain massive flexibility—imagine being able to upgrade your car's navigation system without buying a new car.
Also, a system of systems approach enables capabilities that no single system could achieve.
MOSA provides the technical standards and interfaces that make system of systems practical. If MOSA is the rulebook for how components should connect and communicate, system of systems is the strategy for using those connections to create powerful capabilities.
Any sector facing rapid change or complex threats benefits from an SoS approach. Defense is an obvious example, but we're seeing dramatic results in healthcare (coordinating hospital networks), financial services (detecting sophisticated fraud), and critical infrastructure protection (managing interconnected utilities).
Actually, it's riskier to put all your eggs in one basket. When you have independent systems working together, a problem in one area doesn't crash the entire operation. Plus, you can add new capabilities or replace outdated ones without disrupting the whole network.
Through standardized interfaces and clear protocols—think of it like air traffic control. Each plane operates independently, but controllers coordinate their movements through established procedures. Similar principles apply to system of systems, where defined interfaces and protocols ensure coordinated action.
Three main factors: First, the increasing speed of technological change makes monolithic systems obsolete too quickly. Second, threats (especially in defense and cybersecurity) are becoming more sophisticated and distributed. Third, we finally have the technical standards and computing power to make system of systems approaches practical. It's a perfect storm pushing us toward more adaptable, distributed architectures.
CHAI uses secure adaptability zones that isolate critical operations in air-gapped environments while enabling controlled information flow through standardized interfaces. Every interaction is logged and traceable.
Yes. CHAI's modular architecture allows it to incorporate existing AI capabilities as long as they conform to interface standards. You can build new capabilities around legacy systems rather than replacing them entirely.
Almost any AI capability can become a CHAI module—from language models and computer vision systems to IoT sensors and quantum computing modules. The key is that each module follows standardized interfaces for communication and security.
Implementation time varies based on your needs, but we typically start with a focused pilot that delivers value within weeks while validating the broader architecture. Scaling happens methodically as value is proven.
Unlike monolithic systems that require complete retraining, CHAI allows you to add or update individual modules without disrupting existing operations. New capabilities can be tested in isolation before deployment.
While many approaches break AI into modules, CHAI implements true system of systems principles through standardized interfaces and nested architectures. These standardized connections—aligned with MOSA principles—enable modules to be rapidly reconfigured, updated, or replaced without disrupting the broader system.
Think of it as the difference between having detachable parts and having truly independent, interoperable components. Most modular AI systems are like building blocks that only fit together one way. CHAI modules, through their standardized interfaces, can be dynamically recombined to create new capabilities. A threat detection module might work independently, join a cyber defense ensemble, or become part of a larger gray zone warfare detection system—all while maintaining clear accountability and security boundaries.
This MOSA-aligned architecture enables something unique: entire CHAI ensembles can function as modules within larger CHAI systems. The standardized interfaces make this scalability possible while ensuring secure, traceable operations across all levels of the system.
Every CHAI module maintains clear audit trails of its operations. When multiple modules contribute to a decision, you can trace exactly how each module's insights led to the outcome.
CHAI excels in high-stakes environments where security, accountability, and rapid adaptation are crucial—defense, healthcare, financial services, critical infrastructure, and similar domains.
No. CHAI's modular architecture actually reduces computational requirements compared to monolithic AI systems. It can run on existing infrastructure, with modules activated only when needed.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
