Adept ACT-1
A Large Action Model designed to bridge the gap between human intent and software execution.

The industry-standard container-native workflow engine for orchestrating complex parallel jobs on Kubernetes.
Argo Workflows is a Cloud Native Computing Foundation (CNCF) graduated project designed specifically for Kubernetes. It functions as a container-native workflow engine, enabling users to orchestrate parallel jobs through Directed Acyclic Graphs (DAGs) or step-based sequences. Unlike traditional CI/CD or ETL tools, Argo treats every individual step as a first-class container, providing massive scalability and resource isolation. In the 2026 landscape, Argo Workflows has solidified its position as the backbone for MLOps and high-performance computing (HPC) on Kubernetes, offering native integration with cloud-native storage, secrets, and monitoring stacks. Its architecture relies on Kubernetes Custom Resource Definitions (CRDs), allowing engineers to define complex logic in YAML or through Python SDKs (Hera/Couler). The platform excels in environments requiring cost-efficient resource management, as it leverages Kubernetes' horizontal scaling and spot instance capabilities. As organizations move away from monolithic job schedulers, Argo provides the modularity needed for modern data science pipelines, automated infrastructure provisioning, and high-frequency batch processing. It remains the preferred choice for teams that require deep observability, reusability via Workflow Templates, and strict security compliance within their own infrastructure.
Enables the definition of complex dependencies between tasks where execution follows a non-linear path.
A Large Action Model designed to bridge the gap between human intent and software execution.
Autonomous meeting orchestration that converts verbal commitments into executed workflows across your tech stack.
The action-oriented AI engine that turns meeting transcripts into automated CRM workflows and personalized follow-ups.
Autonomous Scheduling Agent for Hyper-Scale Calendar Coordination
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Built-in support for capturing, versioning, and visualizing output files (S3, GCS, Artifactory) directly in the UI.
Reusable workflow definitions that can be stored in the cluster and referenced across different projects.
Native synchronization primitives to control concurrency and resource access across different workflows.
A separate dependency that allows triggering workflows based on external events like GitHub PRs or S3 uploads.
Native Prometheus metrics for tracking workflow duration, failure rates, and resource consumption.
Granular control over task failures, including exponential backoff and cleanup tasks.
Orchestrating GPU-intensive training jobs while ensuring data preprocessing is completed first.
Registry Updated:2/7/2026
Export model weights and performance metrics to an artifact store.
Processing massive genomic datasets that require hundreds of parallel, dependent steps.
Creating complex environments involving Terraform, Helm, and custom scripts in a specific order.