I design and build governed AI systems for high-trust and enterprise environments.

My work focuses on turning ambiguous product and operational requirements into traceable architectures, bounded execution workflows, validated MVPs and audit-ready engineering artifacts.

I work across AI systems architecture, context engineering, runtime governance, multi-agent orchestration and technical product strategy.

At Agentis Studio, I develop and incubate systems in areas including:

• Enterprise AI runtime governance and execution authority
• AI evaluation, evidence and reproducibility systems
• Privacy-preserving and local-first applications
• Regulated-domain workflow software
• Multi-agent engineering and product-delivery infrastructure
• Learning, readiness and decision-support platforms

Some projects remain private or commercially restricted. Public repositories are selected to demonstrate engineering methods, architecture and validation discipline without exposing confidential product assets.

• Design spec-driven AI and software architectures
• Engineer governed execution across models, agents, tools and external systems
• Define policy, authorization and execution-contract boundaries
• Build deterministic control layers around probabilistic AI systems
• Design context, memory and evidence models for multi-agent workflows
• Develop local-first and privacy-preserving systems when the use case requires them
• Coordinate research, architecture, implementation, review and validation
• Convert early-stage concepts into demonstrable, technically documented MVPs
• Produce traceable engineering documentation suitable for review and due diligence

• AI Runtime Governance
• Context Engineering
• Multi-Agent Orchestration
• Agent, Tool and Provider Authority Boundaries
• Execution Contracts and Policy Enforcement
• Evidence Lineage and Replay
• Local and External Model Integration
• Retrieval and Grounded AI Systems

• Specification-Driven Development
• Deterministic Validation Pipelines
• Architecture and Implementation Reviews
• Traceability and Reproducibility
• Fail-Closed System Design
• Audit-Ready Documentation
• Human and Agent Review Gates

• TypeScript
• Python
• React and Next.js
• Node.js
• PostgreSQL
• Git and GitHub
• Local and hosted LLMs
• Retrieval systems
• Multi-model engineering workflows

An AI system being capable of an action does not mean it is authorized to perform it.

Models may be probabilistic. Policy, authorization, validation and evidence should not be.

Important decisions should be traceable to their inputs, policies, approvals, execution boundaries and resulting evidence.

Clear contracts, constraints and responsibility boundaries reduce implementation risk and make systems easier to review.

The correct architecture depends on privacy, capability, sovereignty, latency and operational requirements. Governance must remain independent of the selected provider.

Agents need bounded authority, controlled context, tool restrictions, approval gates and observable execution history.

Public repositories are selected demonstrations of architecture, engineering discipline and product-development methods.

Commercially sensitive projects, private datasets and active venture assets remain outside the public GitHub surface.

• LinkedIn: https://linkedin.com/in/elói-ramos
• GitHub: https://github.com/EloiRamos

I am interested in conversations around enterprise AI, runtime governance, agentic systems, automation and specification-driven engineering.