AI agents that reach production

Traditional GenAI is an LLM responding to prompts (chat). Agentic AI is a system where the LLM reasons, plans actions, invokes external tools (APIs, databases, systems) and learns from the result. The difference is the ability to act on the world, not just generate text. Read more in our post on MCP explained.

Digital transformation is no longer a technology project — it is a change in how the business operates and decides. Generative AI is its most visible engine: it automates knowledge work, speeds up customer service and frees up team time. But it only pays off on solid foundations: a well-architected cloud and trustworthy data. At Caleidos we approach digital transformation in three connected layers — cloud strategy as the base, data engineering so information is ready, and productive AI agents on top. That way the investment turns into measurable results, not pilots that never scale.

Generative AI already creates value in concrete cases: assistants that answer using your organization knowledge (manuals, policies, ERPs, CRMs), copilots for service and sales teams, automatic summarization and classification of documents, and automation of text-intensive tasks. The pattern that works is to start with a clear-ROI use case, take it to production with rigorous evaluation, and expand from there. A good starting point is AI virtual assistants for customer service.

We do a 2-3 week Agentic AI Readiness Assessment: identify use cases with estimated ROI, evaluate data and API maturity, and propose a first agent that can reach production in 90 days. See the practical example with Strands Agents.

Typical Caleidos stack: Amazon Bedrock for the models (Claude, Llama, Nova), Strands Agents or LangGraph as framework, Bedrock AgentCore for productive runtime, MCP Servers for reusable tools, Knowledge Bases for RAG, Guardrails for output safety. See the full deploy in our Bedrock AgentCore post.

Mainly those available on Amazon Bedrock: Claude (Anthropic) — our default for reasoning quality; Llama (Meta), Mistral, Amazon Nova per case. We choose based on cost, latency and compliance. For very sensitive data, we evaluate self-hosted models on SageMaker.

Yes, when well architected. Bedrock doesn't train on your data. Information travels encrypted and is processed in your AWS account. For regulated data we implement VPC endpoints, Bedrock guardrails, full logging and, when applicable, per-tenant separation.

Mandatory evaluation layer. We combine: RAG over verified data, Bedrock guardrails, output validation with rules and/or LLM-as-judge, and human-in-the-loop for critical cases. Quality metrics reported weekly. Without evaluation, an agent in production is a risk.

Scope and investment are defined with you after understanding the case. Let's have a conversation to put together a tailored proposal based on your context, systems to integrate, and operating model.

Anthropic Claude is natively available on AWS Bedrock — we use it extensively, it is our default model. OpenAI and Google we integrate via API when the case justifies it, but by default we recommend staying in the AWS ecosystem for cost, latency and compliance reasons.

Vision AI is a dedicated service at Caleidos — industrial computer vision with Amazon Rekognition, custom SageMaker models, IP camera integration, YOLO-style detection. Learn more at /en/services/vision-ai.

Caleidos Lens© 24×7 includes LLM observability (latency, errors, drift, costs), retraining schedule, model version management, MCP Server management and on-call for incidents. We operate with you long-term.