Gartner predicts that over 40% of agentic-AI projects will be canceled by 2027—a stark reflection of the artificial intelligence industry’s crisis of credibility. Agent washing, the practice of rebranding basic automation tools as sophisticated AI agents, has become endemic as companies prioritize marketing appeal over operational value. This is particularly problematic in environments where disruptions to mission critical applications and services can have life-threatening consequences.
At Greymatter, we’ve taken a fundamentally different approach. While the industry chases large language model integration for demos, we’ve built autonomous orchestration infrastructure that delivers genuine agentic capabilities through self-provisioning and governing Zero Trust Networking across all applications, APIs, databases and microservices. Our strategic focus on foundational stability and security firstaddresses real operational challenges, particularly in mission-critical environments where neither security nor reliability are negotiable.
Mission-Critical Considerations: Where Disruption Means Danger
Our customer base includes organizations where downtime and disruptions can have life- threatening consequences. In defense environments, application, APIs, and data service vulnerabilities can compromise mission-critical operations that support military forces worldwide. In these contexts, stability and reliability take precedence over feature novelty. Our customers don’t need chatbots that can query their infrastructure—they need systems that can autonomously maintain zero-trust security postures, recover from failures, and adapt to threats without human intervention.
The Agent Washing Problem: Marketing Over Substance
Agent washing represents a fundamental misrepresentation of what makes systems truly agentic. The AI Now Institute first defined this deceptive marketing tactic in 2019, but the problem has accelerated as companies rush to capitalize on AI hype. Recent research shows that 42% of enterprises report AI project failures due to poor infrastructure readiness, while 38% face increased operational costs from failed, often rushed AI initiatives.
The core issue isn’t just exaggerated capabilities—it’s the fundamental infrastructure gap that superficial AI implementations create. Consider the pattern emerging in our space: solutions like Kong’s MCP server and Apache APISIX’s MCP server are essentially direct API translation layers that enable natural language queries but provide no operational control, policy enforcement, or autonomous decision-making.
These implementations share three critical limitations that expose the agent washing problem:
- Query-centric rather than action-oriented architecture: They translate requests into API calls without autonomous reasoning or decision-making capabilities.
- No operational intelligence: They lack autonomous healing capabilities, environmental awareness, or goal-oriented behavior that define genuine agentic systems.
- Missing enterprise-grade security orchestration: They inflate expectations without solving integration, security, or governance challenges, leaving enterprises with expensive prototypes that can’t scale to production.
True agentic systems differ fundamentally from these implementations. As defined by experts, agentic AI systems must “perceive their environment, reason about it, and take actions to achieve specific goals” through autonomous decision-making capabilities, environmental awareness, and goal-oriented behavior.
Autonomous Infrastructure: The Foundation First
At Greymatter, we understand that autonomous infrastructure must precede real agentic AI. Our orchestration fabric embodies genuine agentic principles through:
Environmental perception via continuous monitoring of network topology, service health, and security posture across hybrid and multi-cloud environments. Our system maintains real-time awareness of the entire application, API, and microservice landscape.
Goal-oriented reasoning through our Greymatter Specification Language (GSL), a domain-specific language built on CUE that enables declarative intent specification. GSL allows operators to define high-level objectives, which our orchestration layer then translates into concrete infrastructure actions.
Autonomous execution through self-provisioning capabilities that automatically deploy control planes, proxies, gateways, load balancers, trust chains, and zero trust security certificates without manual intervention. The system responds to environmental changes by reconfiguring itself to maintain desired state.
Information gathering through feedback loops, telemetry, and audit traces that monitor deployment outcomes that in the future can be used for real agentic AI agents that reason provisioning, policy and management based on success patterns and failure modes.
This approach delivers immediate value: reduced time-to-market, standardized application governance, streamlined workflows, improved data management and connectivity, and substantially reduced development costs and complexity while preparing for real agentic AI value. These benefits emerge from solving foundational orchestration challenges rather than layering conversational interfaces onto existing systems.
Customer-Centric Development: Solving Real Problems
Our approach to AI integration exemplifies customer-centric development—placing customer needs at the heart of technology decisions rather than pursuing technology for its own sake. This methodology prioritizes creating meaningful, value-driven experiences over impressive demonstrations.
As global AI regulations tighten and enterprise sovereignty becomes non-negotiable, organizations require infrastructure that maintains full control over their data, models, and decision-making processes. Recent surveys show that 69% of enterprises will consider AI and data sovereignty mission-critical within three years, driven by regulatory compliance requirements and the need to break free from data silos.
Our Greymatter Specification Language (GSL) serves as the crucial abstraction layer that will enable sophisticated AI integration. GSL’s mix-in pattern and composition-based approach provides the structured, declarative foundation necessary for AI systems to understand and manipulate application networking intent. It enables high-level reasoning about zero trust and networking goals. This abstraction layer allows future AI components to:
- Reason about infrastructure intent rather than configuration syntax
- Compose complex deployments from reusable, validated patterns
- Maintain consistency across diverse environments and use cases
- Validate changes before implementation to prevent disruptions
This foundation enables internal reasoning systems that can understand infrastructure requirements, predict failure modes, and recommend optimizations while maintaining complete operational control and data sovereignty.
By working directly with customers in mission-critical environments, we’ve learned that foundational stability enables AI innovation, not the reverse. Our customers need platforms that can reliably orchestrate complex, distributed systems before they can safely add reasoning capabilities on top.
Looking Forward: Agentic Infrastructure as our AI Foundation
The enterprise AI landscape desperately needs infrastructure platforms that can autonomously manage complexity while providing stable foundations for intelligent capabilities. Our approach—building genuinely agentic orchestration systems before adding LLM capabilities—offers several strategic advantages:
- Proven reliability in mission-critical environments where failures have severe consequences
- Complete sovereignty over data, models, and decision-making processes
- Customer-validated value through operational improvements rather than demonstration appeal
- Scalable architecture that can incorporate AI reasoning without compromising stability
- Regulatory alignment with emerging frameworks for AI in critical infrastructure
Our roadmap includes Small Language Model (SLM) integration specifically designed for agentic zero trust orchestration while ensuring enterprise sovereignty. Unlike large language models that require external API calls and cloud dependencies, SLMs can operate entirely within controlled environments.
The strategic advantage in our approach lies in our ability to provide intelligent reasoning capabilities while allowing customer’s to preserve complete data control. They can be fine-tuned on domain-specific datasets, deployed in air-gapped environments, and integrated with existing security frameworks without introducing external dependencies that compromise operational security or sky rocketing costs.
As the industry moves beyond agent washing toward truly agentic AI systems, organizations need partners who understand that genuine agentic capability emerges from solid engineering foundations, not conversational interfaces layered over existing problems.
The future belongs to organizations that build autonomous orchestration thoughtfully, deliberately, and in service of real customer needs. At Greymatter, we’re committed to that vision— putting customer success and operational excellence ahead of marketing metrics, and building the foundation that will enable the next generation of truly intelligent systems.
