Making every agentreliable, replayable,and accelerated.
Maximlabs builds the production infrastructure layer that AI agent deployments are missing. Zero-code, zero lock-in, NVIDIA-native.
Works with any framework
LangChain
CrewAI
LangGraphAI agents are being deployed at scale. The infrastructure to run them reliably does not exist.
State loss
Most runtimes treat every agent call as stateless. Context loss mid-task, infinite loops, and wasted compute are the norm.
Debugging is impossible
No native way to replay a failed agent run without heavy SDK instrumentation. Developers are flying blind in production.
GPU waste
Bursty agent workloads lead to 20–40% GPU utilization. Cold starts and KV-cache reloads destroy the economics at scale.
The missing production runtime layer.
A drop-in infrastructure wrapper that works across any framework, any provider, and any agent codebase — without requiring code changes.
Persistent Sessions
Stateful context across every agent step. No more context loss, no more infinite loops. Sessions survive restarts.
Exact Replay
Re-execute any past agent run step by step, with different models or configs. Like git bisect for AI agents.
NVIDIA-Native
Built for the NVIDIA AI ecosystem. NIM integration, KV-cache optimization, GPU utilization tracking, and ROI calculation.
We run on our own product.
Every automated function at Maximlabs runs through Anchor — persistent sessions, full observability, and zero framework lock-in.
Engineering
Code review, pull request triage, and test coverage analysis
Operations
Infrastructure health monitoring and incident response routing
Support
Inbound query triage and onboarding workflow coordination
Growth
Market research, audience analysis, and pipeline qualification
Content
Drafting, editing, and publishing across internal channels
of agentic AI projects will be canceled by end-2027 due to escalating costs and inadequate production infrastructure.
Gartner, June 2025projected agentic AI market by 2030, growing at 40–46% CAGR.
IDC / Gartner / Zinnovof companies plan agent deployment within two years, but production readiness lags significantly.
Deloitte, 2026Be the first to deploy.
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