The Demand for Truly Autonomous AI
Levangie Laboratories uncovered a major shortfall within the autonomous AI industry.
Organizations with billions in funding and hundreds of thousands of NVIDIA GPUs were optimizing language models to have better conversations, write more polished emails, and generate cleaner code snippets. But when it came to actual autonomy, the entire industry was stuck in the same paradigm.
AI that could operate businesses, manage critical systems, and make decisions without waiting for human approval simply didn’t exist.
That’s when Brayden Levangie, founder of Levangie Laboratories (LLABS), started building cognitive architecture that enables AI agents to operate independently, learn from experience, and adapt to organizational needs without human intervention.
Levangie Labs coins it: The “Intel Inside” of AI business operations.
With clientele spanning healthcare operations, spatial computing platforms, legal tech patent systems, enterprise supply chain, real estate automation, and financial advisory…these forward-thinking organizations needed production-grade AI capabilities without hiring massive AI teams or spending years in development.
But there was a fundamental contradiction in their mission. To power intelligence that governs itself, learns from episodic memory, and rewrites its own code in real-time, you cannot rely on infrastructure that requires human operators to provision compute resources. Traditional cloud platforms created dependence through rigid, centralized architecture when autonomous AI requires frictionless, self-governed power.
Enter: Akash Network
Facing expensive reserved capacity models, vendor lock-in risks, and single points of failure with traditional cloud providers, LLABS deployed their sophisticated multi-container cognitive architecture on Akash Network’s decentralized compute marketplace.
By distributing workloads across multiple independent providers globally, Akash eliminated the centralized dependencies that made true autonomy impossible. Infrastructure costs aligned with actual usage patterns rather than over-provisioned reserved capacity.
As a result, LLABS achieved “agents scaling agents” capability where their AI systems autonomously manage their own infrastructure through the Akash API.
Traditional Cloud Infrastructure vs. Autonomous AI Requirements
Before Akash, LLABS faced three critical infrastructure challenges that directly conflicted with their mission of building truly autonomous AI agents.
Cost & Scalability Mismatch
Traditional cloud providers locked LLABS into expensive reserved capacity models that failed to align with variable AI workload patterns. Over-provisioning meant paying for unused compute during low-demand periods. Under-provisioning risked performance degradation during peak demand. Neither approach delivered the efficient scalability required for autonomous agent operations.
AI agents need infrastructure that scales dynamically based on actual demand, not pre-purchased capacity commitments that benefit cloud providers rather than customers.
Vendor Lock-In Constraints
Being tied to provider-specific services and pricing structures threatened to limit LLABS’s flexibility as they scaled across multiple enterprise verticals. Building on AWS-specific managed services or GCP-proprietary tools created technical debt that would become increasingly expensive to unwind as the company grew.
For a company positioning itself as infrastructure for autonomous AI, being dependent on a single vendor’s ecosystem was both philosophically inconsistent and strategically risky.
Single Points of Failure
The recent major outages on platforms like AWS highlighted the fundamental risk of centralized infrastructure. When a single provider experiences downtime, entire segments of the internet go dark. For AI agents designed to operate continuously and autonomously without human intervention, this centralization represented an unacceptable single point of failure.
Autonomous AI systems cannot be truly autonomous if their underlying infrastructure depends on a single entity’s operational reliability.
LLABS needed infrastructure that was cost-effective, truly scalable, and resilient against the centralization risks that plague traditional cloud providers.
What Made This Especially Challenging
LLABS’s cognitive architecture is a complex simple workload. The production system requires:
- Real-time coordination between specialized AI components
- Complex networking configurations for inter-container communication
- Low-latency data exchange for cognitive processing
- Persistent memory and state management across distributed nodes
Decentralized Compute Was a Non-Negotiable
Akash Network’s decentralized compute marketplace solved all three challenges through fundamentally different infrastructure architecture.
Distributed Resilience Model
Instead of depending on a single cloud provider’s uptime, Akash distributes workloads across multiple independent providers globally. No single provider outage can take down LLABS’s entire infrastructure. This distributed architecture aligns perfectly with the resilience requirements of autonomous AI agents that must operate continuously.
Market-Based Pricing
Akash’s open marketplace model allows compute resources to be priced based on actual supply and demand rather than arbitrary tier structures and reserved capacity commitments. Infrastructure costs align with usage patterns. Compute becomes a true commodity rather than a vendor relationship requiring capacity planning and contract negotiations.
Provider-Agnostic Architecture
Building on Akash required LLABS to adopt containerized, portable architectures without relying on provider-specific managed services. This discipline eliminated vendor lock-in and created more flexible systems that can deploy across any infrastructure that supports standard container orchestration.
Deployment Success
LLABS deployed their full cognitive architecture on Akash. The decentralized infrastructure handled complex multi-container workloads with the performance characteristics required for production AI operations, proving that distributed compute can support enterprise-grade AI systems without technical compromise.
The Breakthrough: Agents Scaling Agents
The most significant milestone was implementing auto-scaling for agent workloads via the Akash API. LLABS built automation that allows their AI agents to provision additional compute resources dynamically based on demand.
This “agents scaling agents” capability means LLABS’s AI systems can autonomously manage their own infrastructure without human involvement. When an AI agent detects increased workload demand, it programmatically calls the Akash API to provision additional compute resources. When demand decreases, resources scale down automatically.
Why This Matters
Achieving autonomous infrastructure management on decentralized infrastructure validated both Akash’s API capabilities and LLABS’s architectural approach. Truly autonomous AI operations are possible on distributed compute, demonstrating that decentralization is not just a philosophical preference but a technically viable foundation for next-generation AI systems.
Community-Driven Innovation
One of the most unexpected benefits of building on decentralized infrastructure: it evolves based on what the community needs, not what a corporation’s product roadmap dictates. LLABS found this leads to more innovative solutions and faster iteration on features that actually matter to developers building production systems.
What’s Next
LLABS is increasing its presence in the AI research community through technical workshops and presentations. The company is positioned as a leader in cognitive architecture and agent autonomy, building relationships with leading architecture firms, the spatial computing ecosystem, and top-tier research institutions.
To explore LLABS’s cognitive architecture in detail and follow their latest developments on X.
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