A reusable reference framework that lets teams build agents without locking into any one model or orchestrator. Built on Amazon Bedrock AgentCore (Runtime, Identity, Gateway, Memory, Observability) with an AI Agents Studio that plugs into Strands, LangGraph, CrewAI, n8n, Vertex, and OpenAI, fronted by an Orchestrator and an internal Agent Marketplace.

A hub-and-spoke Model Context Protocol layer that turns tools and external systems into discoverable, governed capabilities. AgentCore Gateway handles discovery and invocation against a central Tools Repository, while the MCP layer brokers access to first- and third-party systems: AWS Marketplace, Azure Copilot, and partner platforms.

A retrieval layer engineered for accuracy at scale: hybrid retrieval that runs dense vector search and sparse lexical (BM25) together and fuses their rankings with Reciprocal Rank Fusion, then cross-encoder reranking and Maximal Marginal Relevance for relevant, non-redundant context. It runs over an Amazon OpenSearch vector database, with content prepared using Unstructured.io, per-use-case model selection via the Bedrock Model Catalog, and Bedrock Guardrails plus prompt governance keeping answers safe and grounded.

An end-to-end MLOps practice with CI/CD and continuous training: automated pipelines for data validation, training, evaluation and registration, continuous delivery to serving, and production monitoring that triggers automated retraining. Agents can create, train and retrieve models through the same pipeline. It also covers model fine-tuning and compression: supervised fine-tuning and teacher-student knowledge distillation to shrink large models into smaller, cheaper, lower-latency students that retain quality for production serving.

The governed data foundation everything draws on: a Snowflake lakehouse on a Bronze to Silver to Gold medallion architecture, transformed with dbt, catalogued and lineage-tracked, and surfaced through a knowledge-graph database for relationship-rich retrieval.

An AI-assisted acquisition pipeline that pulls from many sources: databases, files, PDFs, reports, and the web. It then synthesizes them, infers and generates a schema, and schedules ingestion with AI in the loop. Content is extracted and chunked through Unstructured.io and embedded at scale into the vector store.

An AI Development Life Cycle with AI applied at every step, organized as two loops. In the fast inner loop, engineers work in an AI-enabled IDE: scaffolding from golden-path templates, AI pair-programming, and a shift-left approach that pulls tests, security and evals to the moment of authoring. The outer loop covers CI/CD, infrastructure-as-code (AWS CDK + GitHub), evaluation and observability (Langfuse, AgentCore), and production feedback that flows back into the inner loop. The result: AI ships repeatably and responsibly.

A repeatable playbook for converting monoliths to modern systems without a risky big-bang rewrite. Domain-Driven Design carves the monolith into bounded-context microservices; the Strangler Fig pattern routes traffic incrementally from legacy to new services behind a facade until the old system can be retired; and a micro-frontend shell composes independently-deployable UIs per domain.

Every capability is an independent, composable building block, usable alone or compounded into complex workflows, rooted in business need. Golden Paths and reusable SDKs abstract the heavy lifting (security, compliance, infra, data, identity) so teams focus on differentiation.

A persona-aware experience layer with deep UX customization and contextual agents at every layer, from intelligent search and interactive exploration, to drag-and-drop generative widgets, to agent-built dashboards and custom apps on the same governed data platform. The agent-to-UI contract is built on open standards: AG-UI for streaming agent-to-frontend events and generative components, and A2UI for declarative, model-rendered interfaces. Any compliant agent or model can drive the UI without custom glue code.

A unified control plane to build, deploy and orchestrate intelligent agents: one-click agent creation, a tool and connector registry, a visual workflow builder for multi-step orchestration, integrated chat for testing, and embedded SDLC with launch.

An always-on operations teammate that autonomously investigates incidents and remediates across the stack: alert ingestion, then triage & correlation, then root-cause analysis, then auto-remediation, then a prevention spec, all wired into the observability and alerting stack.