By Sofiya (Cori) Zaichyk
Teams working with autonomous systems often encounter the same fundamental challenge. They must coordinate and verify many independent systems operating with partial information while maintaining assurance across the overall system.
Different systems see different parts of the environment. Communications may be delayed or intermittent. Vehicles, sensors, and software agents can come online or drop out unexpectedly.
Yet the overall system still needs to maintain a coherent picture of what is happening, respond in a coordinated way, and behave as intended.
Customers working in defense and national security environments see these challenges every day. Missions rarely unfold with perfect connectivity or a central authority that can reliably coordinate every action. Systems from multiple vendors must often be integrated into existing mission threads.
Evaluating how those systems behave together, especially in degraded environments, can quickly become infeasible to test exhaustively.
The difficulty goes beyond making individual agents smarter. It means ensuring that many independent components can maintain a shared understanding of the environment while also allowing teams to verify how the overall system will behave.
Rethinking Coordination in Distributed Systems
That is what led me to explore an approach called Semantic Fusion, which looks at how decentralized systems can coordinate through a shared representation of the environment while putting explicit safeguards on what is allowed to enter that shared state.
You can read the full ACM Transactions on Autonomous and Adaptive Systems paper on Semantic Fusion here: https://dl.acm.org/doi/10.1145/3797042
Semantic Fusion uses a structured representation of knowledge called an ontology to define what entities exist in the system, how they relate to one another, and what updates are considered valid. Instead of treating system state as an open stream of updates, the approach treats shared knowledge as structured semantic memory.
Agents can propose changes to that shared information, but those changes must satisfy defined constraints before they are accepted. In practice, this means the system enforces rules about identity, relationships, and permissible transitions.
This semantic structure does more than support coordination. It enables scalable verification of complex systems.
When components expose clear semantic commitments about their behavior, teams can reason about and verify system behavior compositionally, enabling scalable system assurance without needing to test every interaction between components.
To keep the work grounded, I simulated this approach to observe how it behaved when updates arrived asynchronously under imperfect communications. Updates that violated the defined constraints were rejected before entering the shared state, allowing agents to operate independently while still contributing to a consistent picture of the mission environment.
Why This Matters for Real Systems
These ideas matter because autonomous systems are scaling faster than our ability to test them. Large systems-of-systems can contain thousands of potential interactions, making exhaustive testing impractical.
For customers integrating multi-vendor capabilities into operational mission threads, this creates a major evaluation challenge. As programs adopt modular open systems approaches (MOSA) to reduce vendor lock and enable greater flexibility, the number of possible component interactions continues to grow.
Semantic structure offers a different path: verify behavior through the commitments components make about shared state, rather than trying to enumerate every interaction among components.
At IDT, this idea is already being applied in practice. Mission semantics can serve as an organizing layer for scalable assurance in complex, adaptive systems-of-systems. This is particularly relevant to today’s acquisition environment, where integration across vendors into existing mission threads is the norm.
As autonomous systems continue to scale and become more interconnected, we’ll need approaches that make coordination and verification work under real constraints: partial information, intermittent communications, and heterogeneous components. Combining semantic coordination with compositional verification is one concrete way to make large autonomous systems more predictable, testable, and trustworthy.
Sofiya (Cori) Zaichyk is an AI researcher at IDT working at the intersection of statistical learning theory, cognitive system design, and complex systems. Her work focuses on distributed decision-making, semantic modeling, and approaches for building verifiable AI-enabled systems operating in complex, real-world environments.