Feature: AI
Figure 2: Classical OS vs. LMOS
tools, build pipelines and monitoring systems. And, businesses gain resilience, accelerating innovation without adding operational fragility.
of their systems in Python or TypeScript, languages commonly associated with AI experimentation. But that path leads to what engineers call the “rewrite trap”. Every time a new technology wave arrives, organisations are tempted to rebuild what already works. In the process they lose the reliability, scaleability and compliance guarantees that have been built up over time in their existing platforms. For AI initiatives, that trap is even more dangerous because: • AI workloads amplify complexity: Each new model, agent or pipeline adds operational dependencies and failure modes.
• Compliance burdens increase: Rewritten systems may not inherit the security controls and auditability of legacy Java services.
• Costs rise exponentially: Rebuilding high-throughput transactional logic on LLM-based infrastructure is oſten 10-100 times more expensive than invoking an optimised Java process. Te more sustainable approach is to
augment, not replace. By enabling agentic AI to interoperate directly with Java, enterprises can modernise incrementally, adding intelligence and automation where it matters most, without jeopardising core systems. To make this integration work, the industry is gravitating toward a hybrid runtime model in which LLMs handle reasoning and language understanding, whilst Java runtimes handle concurrency, reliability and deterministic execution. Here, an agent can’t use natural-language reasoning to determine intent or plan an action sequence, then delegate execution to a Java-based microservice or actor system. Tis separation ensures that LLM-driven
agents remain stateless, scaleable and explainable, and that critical transactions and state transitions remain in trusted, observable Java environments. Te result is an architecture that combines the creative flexibility of AI with the engineering discipline of Java, offering the best of both worlds: intelligent automation that operates at enterprise scale without sacrificing control.
Modernisation via interoperability Enterprises don’t need to abandon their Java ecosystems to participate in the AI revolution. In fact, the opposite is true: Java may be their strongest enabler for adopting agentic AI responsibly. Recent innovations in the JVM
ecosystem, such as Project Loom for lightweight concurrency, GraalVM for polyglot execution and Kotlin/Scala interoperability, make it easier than ever to embed AI agents within Java-based environments. Tese tools allow enterprises to run LLMs or agentic frameworks as co-resident services alongside existing Java microservices, expose Java classes and actors as callable actions for AI agents, and maintain unified observability and governance across both human-coded and AI-generated workflows. Tis approach doesn’t just preserve prior investments; it multiplies their value. Every reusable Java component becomes a potential building block for intelligent automation. By grounding agentic AI in proven
Java systems CIOs gain predictability, ensuring AI actions respect existing SLAs and compliance boundaries. CTOs gain flexibility, enabling experimentation without duplicating infrastructure. Developers gain leverage, using familiar
Eclipse LMOS A good solution that enables developer teams to use their existing Java assets is the Eclipse LMOS (Language Model Operating System) project; see Figure 2. Tis is an open source platform for orchestrating intelligent AI agents that perform complex tasks at enterprise scale. Its goal is to create a sovereign platform where AI agents can be developed, deployed and integrated seamlessly across networks and ecosystems. For enterprises with decades of investment
in Java, LMOS makes it possible to build agents without reinventing the soſtware stack. Teams can reuse their existing libraries, frameworks and expertise. Te same people who understand the domain can now build agents directly. Eclipse LMOS enables enterprises to
build on what they already have. It reuses existing infrastructure, DevOps tooling and libraries rather than forcing teams to reinvent everything in a new stack. Tat means no new teams to hire, no duplicated environments to maintain, and no coordination overhead across multiple tech silos. Te result is faster iteration, lower costs and a much shorter path from prototype to production.
Intelligent automation Agentic AI represents the next great leap in enterprise automation, with systems that can reason, act and collaborate autonomously. But intelligence alone is not enough. For these systems to deliver real value, they must integrate deeply with the technologies that already power the world’s businesses. Java remains the heartbeat of that
world. Te future of enterprise AI will not replace Java – it will extend it intelligently. Organisations that embrace this hybrid, interoperable approach will not only unlock the full potential of agentic AI, they will do so with the confidence, governance and performance that only decades of engineering and operational experience can provide.
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