Build production agentic AI workflows — multi-step, tool-using, autonomous — for customer support, ops, and knowledge work.
Talk to an AI workflow architectAgentic AI is the next step beyond chatbots. Instead of answering a question, an agent takes multiple steps, uses tools (APIs, databases, code execution), and completes a task autonomously. Customer support agents that resolve tickets end-to-end, ops agents that investigate and fix incidents, knowledge agents that research and synthesize — these are agentic workflows.
CB4UHost's Agentic AI Workflows service builds production agents using frameworks like LangGraph, CrewAI, Microsoft AutoGen, and OpenAI Assistants. We handle the full stack: agent design (prompts, tools, control flow), evaluation (does the agent actually solve the task?), safety (guardrails, human-in-the-loop, audit trails), and deployment (scalable serving, observability, cost control).
We're framework-neutral. LangGraph for complex multi-agent workflows, CrewAI for role-based collaboration, AutoGen for code-heavy agents, OpenAI Assistants for simplicity. We recommend based on your use case.
Every engagement ends with a production agent, eval suite, observability dashboards, and runbooks so your team can extend and operate it.
We assess your use case and recommend the right agent architecture and framework.
We design the agent — prompts, tools, control flow, human-in-the-loop checkpoints.
We integrate the agent with your systems — APIs, databases, code execution, web search.
We build an eval suite that tests the agent on real tasks — so you know it works before production.
We add guardrails (content filtering, action limits, human approval for risky actions) and audit trails.
We deploy with scalable serving, observability, and cost control.
We assess the use case and pick the right framework and architecture.
We design the agent and integrate it with your tools.
We build the eval suite and iterate on the agent until it meets quality targets.
We add guardrails and deploy to production.
We hand over runbooks and set up ongoing monitoring.
Interactive tools related to this service.
A chatbot answers questions using an LLM. An agent takes multi-step actions using tools — it can call APIs, query databases, execute code, and complete tasks autonomously. A chatbot says 'here's how to reset your password'; an agent resets your password for you.
LangGraph for complex multi-agent workflows with explicit state management. CrewAI for role-based collaboration (multiple agents with different roles). AutoGen for code-heavy agents. OpenAI Assistants for simplicity. We recommend based on your use case.
Three layers: (1) guardrails (content filtering, action limits), (2) human-in-the-loop for risky actions (large refunds, customer-facing comms, data deletion), (3) audit trails so every agent action is logged and reviewable. Agents never have unrestricted access.
No — agents augment humans. They handle the repetitive 80% so humans focus on the complex 20%. Customer support agents resolve tier-1 tickets so humans handle escalations. Ops agents investigate incidents so humans fix them. The goal is capacity, not replacement.
Adapt open-source LLMs (Llama, Mistral, Qwen, DeepSeek) to your domain — with LoRA, QLoRA, instruction tuning, and safe deployment guardrails.
Production retrieval-augmented generation with vector stores, hybrid search, reranking, citation, and observability — so you can trust the answers.
End-to-end ML pipelines — experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Built on Kubernetes.
Tell us about your project. We'll come back with a scoped proposal and a fixed-fee quote.
Talk to an AI workflow architect