Move from agent prototypes to production workflows with orchestration, evaluation, monitoring and deployment control across enterprise AI systems.
Content
Problem
AI and ML engineering teams often need to bridge the gap between agent experimentation and reliable production operation.
Context
Production agents require runtime primitives, deployment choices, evaluation and operational visibility in addition to model and prompt development.
Data sources
Relevant sources can include model providers, tools, APIs, databases, enterprise applications and agent runtime infrastructure. Exact integrations must be validated for each environment.
Approach
thaink² provides the commercial agent platform and AgentOps layer, while aPowerB provides an open-source runtime for building and running agent workflows.
Capabilities
Agent orchestration
Evaluation workflows
Monitoring and usage visibility
Multi-LLM runtime support
Self-hosted deployment paths
Expected Results
Give AI engineering teams a clearer path from agent development to controlled production operation without coupling the entire workflow to one model provider.
