Who is Fernando F. Azevedo?

Fernando F. Azevedo is a Senior Solutions Architect at Banco Itaú with 16+ years of experience across AWS, event-driven architecture, DevSecOps, Data Mesh, AI and financial systems.

What technical topics does Fernando work with?

Fernando works with AWS, Kubernetes, Kafka, Data Mesh, Amazon Bedrock, RAG, DevSecOps, observability, financial systems and architecture communication using C4, ADRs and trade-off analysis.

Is Fernando available for professional conversations?

Fernando is currently building at Banco Itaú and is open to thoughtful conversations about architecture, cloud, AI, engineering leadership, community, podcasts and technical collaboration.

Learning path · Trilha

AI & Agents

Applied AI on Amazon Bedrock — RAG, agents and AgentCore — taken from prototype to production responsibly.

The path· 83

Deep-dive studies

tutorialSAP LeanIX in Practice: The Architect's Guide — Inventory, Diagrams, and ReportsA practical, opinionated tutorial on using SAP LeanIX as a transformation accelerator in the architect's daily work — covering inventory (Fact Sheets, meta model, quality), diagrams (types, when to use, live vs static), and reports (question → data → decision). Uses Hohpe's Architecture Elevator as the connecting thread and a fictional insurer/bank scenario to make every concept concrete.guideLoop Engineering: designing the loops that guide AI agentsThe bottleneck in AI agents has shifted from the prompt sentence to the loop design: trigger, topology, verifier, and stop rules. This guide teaches loop engineering fundamentals — from the base ReAct loop to a pattern catalogue — with real examples from my own system and a step-by-step guide for engineering any loop safely.guideLambda vs ECS: the architect's compute decision guide on AWSChoosing between Lambda and ECS is not about preference — it is about matching the unit of scale to the load pattern. This guide covers every Lambda type (including MicroVMs and Managed Instances) and ECS type (Fargate, EC2, Managed Instances), the decision framework I use in practice, and the real impact on engineering, business, and customer experience.playbookPlaybook: 5 AI Architecture Shifts — and what to do about each oneWhat was advanced in 2025 became default in 2026: the bottleneck moved past the prompt, agents went to managed production, MCP standardized tooling, security became layered, and token FinOps entered the roadmap. This playbook maps each shift to concrete actions — for people building systems, not demos.guideInside AI Agents (1/3): Anatomy and the Reasoning LoopA technical lesson for developers and architects who hear 'AI agent' every day but want to truly understand what differentiates an agent from a plain LLM, a fixed pipeline, or simple RAG. We cover the full anatomy — model, tools, memory, planner — and the ReAct loop step by step with a concrete example. No hype; real trade-offs.guideInside AI Agents (2/3): Architecture Pattern Catalog — from ReAct to Multi-AgentThe second lesson in the series maps the full catalog of AI agent architecture patterns: from single-agent loops (ReAct, Reflexion, Plan-and-Execute) to multi-agent orchestration, covering memory as an architecture decision, guardrails, and human-in-the-loop. The goal is to give the architect a precise vocabulary to choose — and justify — the right pattern for each problem, without falling into classic anti-patterns.

Open source to explore

aws-event-driven-finops-platformA banking event mesh on AWS where picking the messaging service is a cost decision.aws-agentic-ai-reference-architectureAgentic AI on AWS for real: Bedrock, MCP, guardrails and observability.diagram-as-codeGenerate AWS diagrams from YAML — Go CLI with a hosted web frontend.DeskbuddyESP32 touchscreen smart desk dashboard — firmware, web UI, and browser installer in one repo.solution-architecture-mcp-toolkitBilingual MCP toolkit for ADRs, threat modeling, and governed Well-Architected reviews.bedrock-agent-starterA Bedrock agent with tools, memory, metrics and Terraform — running in 30 minutes.