Managed cloud agent platforms

Managed cloud agent platforms help teams move from a working demo to a dependable production agent without building (and operating) a full runtime from scratch. They typically include orchestration, tool execution, retrieval/grounding, permissions, auditability, and safety controls—so agents can run inside real business workflows with clearer governance.

Technologies we support

OpenAI Responses & Assistants APIs

A straightforward way to run tool-using assistants in production, with hosted capabilities like function/tool calling plus built-in tools such as Code Interpreter and File Search for analysis and retrieval.

Anthropic Claude (Computer Use & Skills)

Well-suited for agent workflows that need governed execution across tools and files, including "computer use" for GUI-driven automation and reusable Skills for repeatable tasks.

Google Vertex AI Agents (Vertex AI Agent Builder)

A GCP-native environment for building and orchestrating agents with enterprise controls such as permissions and audit trails, designed for production deployments at scale.

Azure AI Agent / AutoGen Studio

Microsoft's agent workflow ecosystem, including AutoGen Studio for designing multi-agent and tool-based workflows that fit common enterprise patterns on Azure.

AWS Bedrock Agents

A fully managed approach on AWS for agents that call tools/APIs, orchestrate steps, retain context, and apply safety policies via Bedrock Guardrails.

Where this fits

These managed runtimes are a strong match for customer support automation, internal ops assistants, and "copilot" features inside SaaS products—especially when security, observability, and cost controls are non-negotiable. Many teams use them to standardize how agents call tools, access knowledge safely, and behave consistently as usage grows.

What we deliver

  • Platform selection and architecture (OpenAI vs. Anthropic vs. AWS/Azure/GCP) based on constraints like data boundaries, latency, governance, and integration needs.
  • Production engineering: tool design, grounding/retrieval strategy, guardrails, and end-to-end observability so the agent stays predictable in real workflows.
  • Operational readiness: evaluation practices, regression testing, and deployment patterns that hold up as prompts, tools, and product requirements evolve.