Observabilidade5 min
Fundamentals of Observability and Operation for Beginners
Building a system is only half the work. The other half is ensuring it continues to function in production.
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01 / About me
Software & IA Engineer
I'm Alexsander Valente, Software & IA Engineer with over ten years of experience designing systems, focused on LLMs, RAG architectures, multi-agent systems, and data platforms.
My background spans software engineering, data engineering, and applied AI. That overlap lets me work end-to-end: from architecture and API design to data pipelines, MLOps, observability, and model deployment in critical environments.
I have solid experience in the financial sector, where systems fail with real consequences. That context shaped how I design: with a focus on reliability, traceability, and operability.
I work with teams that need to turn AI into a product, not an experiment.
Latest Posts
Check out the latest blog articles with tutorials, analysis and insights on software engineering, data and AI
Dados6 min
Data Architecture Fundamentals for Beginners
When we talk about modern systems, we usually think of APIs, interfaces, cloud, and Artificial Intelligence.
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System10 min
Fundamentals of System Design for Beginners
After understanding the business problem, the product objectives, and the software architecture fundamentals, it's time to take the next step: learning System Design.
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Design
02 / Areas of work
What I do
I work on building systems where software and artificial intelligence operate in an integrated way, from architecture definition to production deployment. I don't treat AI as an isolated feature: I design the entire system, with the backend, data, and interface layers that make the difference between a prototype and a product that actually works.
#01
Artificial Intelligence
I design LLM-based systems where AI has a clear and bounded role: interpreting, classifying intent, and deciding the conversation path. Critical execution — such as pricing, availability, and transactions — stays in deterministic APIs. That separation is not an implementation detail; it is what defines whether the system is reliable in production or not.
Regular work
Multi-agent architecture with isolated domains and explicit orchestration
RAG with groundedness control and continuous quality evaluation
Conversational systems integrated with business rules and ERPs
Layered guardrails that separate what AI decides autonomously from what requires deterministic confirmation
Evaluation and regression control on every model or prompt change
Human handoff where the agent enters with full session context, not from scratch
Corporate copilots integrated with real operational workflows
Structured processing and extraction from unstructured documents
Recurring stack
LangChainLangGraphCrewAIOpenAIAnthropicpgvectorQdrantLangSmith
#02
Software & Data Engineering
I build the layers that make AI actually work in production: APIs with stable contracts that LLMs can call without surprises, integrations with legacy systems that were never designed for this, data pipelines that arrive clean and on time, and infrastructure that scales without becoming technical debt. Most AI problems in production are not model problems — they are engineering problems around it.
Regular work
Deterministic APIs that serve as the source of truth for AI systems
Integration with ERPs, CRMs, and legacy systems with heterogeneous contracts
Microservices with well-defined boundaries and low coupling
Multi-tenant systems with data isolation and per-layer governance
Event-driven architecture with end-to-end traceability
Scalable ETL and ELT pipelines with data quality and governance
Data lakehouse architecture and datasets for machine learning
Cloud infrastructure with observability, CI/CD, and infrastructure as code
Recurring stack
PythonFastAPIDjangoPostgreSQLKafkaDatabricksApache SparkdbtDelta LakeAWSDockerKubernetesTerraform
#03
Frontend & Product
I build the layer that makes the system usable. Conversational interfaces integrated with the AI backend, operational dashboards that reflect the real state of the system, and internal platforms that teams actually use. I care about what happens when the user interacts with something that has AI underneath: perceived latency, loading states, error handling, and context continuity across channels.
Regular work
Conversational interfaces integrated with AI backends with persistent session state
Operational dashboards connected to real-time events and APIs
Internal platforms that abstract technical complexity for non-technical teams
Multichannel experiences with context continuity between web and other channels
Product components connected to data flows and transactional systems
Recurring stack
TypeScriptReactNext.js
#04
Architecture & Technical Consulting
I help teams make structural decisions that do not become problems six months later. I join projects where the technical foundation is still being defined, where an existing architecture needs to evolve to support AI, or where the team needs someone who has already made these mistakes and knows what to avoid. I don't sell technology — I sell clarity about the problem and structure to solve it.
Regular work
Technical diagnosis with identification of real structural risks
Architecture definition for systems that integrate AI and transactional software
Technical feasibility assessment before committing team and budget
Technical MVP structuring with reversible decisions where it matters
Technology roadmap definition with objective evolution triggers
Support for teams adopting AI in production for the first time
Recurring stack
Toolkit
Documentation
Complete documentation kit for enterprise-level Generative AI projects, bringing together reference architectures, technical patterns, essential checklists, and support materials for production-ready solutions.
Documents
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Architectures
08
100% FreeExplore the Documentation
Architecture Guide
Comprehensive patterns for RAG, Agents, LangGraph, and enterprise security.
170+
Pre-Development Checklist
Interactive validation with 170+ items. Export to Markdown.
170+
Technical Glossary
50+ terms with definitions, categories, and related concepts.
50+
ADR Templates
8 specialized templates for GenAI architecture decisions.
8
Contact
Have a software or AI system to evolve?
Software and AI integrated, from architecture to production, with a focus on clarity, reliability and real execution.