Article 49TechniqueSecurity and Governance
Auto-approve is not a neutral convenience
Evidence Brief on HarnessSecurity-Bench, coding-agent authority, and the trade-off among utility, human confirmation, and adversarial effects.
Human authorship · Human knowledge + AI
Fernando Parreiras
Founder of Trustyu · Engineer · Solutions Architect
Fernando Parreiras is the founder of Trustyu, engineer and solutions architect. Connects technology, strategy and human development to build businesses, products and decisions with vision and purpose.
Trajectory
Technology with vision and purpose.
With more than 20 years of experience, Fernando Parreiras works at the intersection between technology, management and human development. As founder of Trustyu and creator of FORGE, he leads the design of architectures, digital products and AI systems that transform complex problems into safe, verifiable and sustainable solutions. His practice combines software engineering, solution architecture, product strategy, governance and team building. As an entrepreneur, advisor, author and strategic mentor, he shares knowledge so that business leaders and technical professionals can make better decisions without separating excellence, ethics, purpose and long-term impact.
- Solution architecture
- Software engineering
- AI Systems
- Product strategy
- Business and leadership
Published in Trustyu Forge
Ideas that become systems.
Article 48TechniqueQuality, Evals and Contracts
The test passed, but did the agent fix or rewrite?
Evidence Brief on the Precise Debugging Benchmark and the difference between passing tests, recovering the expected fix, and avoiding unnecessary changes.
Article 47TechniqueSecurity and Governance
Explainable AI in the software chain: detecting anomalies is not proving security
Evidence Brief on XAI-SCS, anomaly detection in npm and PyPI packages, actionable explanations, and the boundaries between alerting, investigation, and security.
Article 46TechniqueQuality, Evals and Contracts
Specifying before testing improves detection — but costs 38% more tokens
Evidence Brief on contract-driven testing, detection gains on 90 production bugs, and the additional cost of turning intent into evidence.
Article 45TechniqueQuality, Evals and Contracts
Reviewing code with AI requires adversarial knowledge and dynamic validation
Evidence Brief on BlueCodeAgent, adversarial knowledge, dynamic analysis, and the limits of using AI to review code security.
Article 17TechniqueEvidence
The model became cheaper; the system became expensive: the new AI cost chain
The technical AI cost chain, from inference through context, harness, CI, evals, operations, human work, failures, and capacity.
Article 30Career & EntrepreneurshipBusiness and P1–P6
Design Partner is not a market: proximity to learn without inventing PMF
How to learn deeply from a Design Partner without turning intense collaboration into false proof of market or PMF.
Article 29Career & EntrepreneurshipStart here
Seniority in the age of agents: fewer ready-made answers, more standard of proof
Seniority appears in framing, judgment, verification, ownership, multiplication and limits — not in the volume produced.
Article 28BusinessBusiness and P1–P6
The right queue for AI: prioritize decisions and outcomes, not flashy automations
A method for prioritizing AI opportunities by outcome, frequency, friction, verifiability, risk and learning.
Article 44Techniqueengineering harness
FDE without improvisation: the harness that turns the customer mission into a verifiable product
The technical contract that connects mission, discovery, spec, build, evals, production, adoption, and reusable learning in an FDE's work.
Article 27TechniqueQuality, Evals and Contracts
Evals before release: how to transform expectations into executable criteria
Tasks, graders, datasets, trials, baseline and gates make an agent's expectations observable before releasing a change.
Article 26Techniqueengineering harness
Context engineering: what deserves to enter, leave and survive the session
How to select, structure, retrieve, compress, externalize and expire context without confusing chat with durable memory.
Article 25BusinessWhy FORGE
The first product is not just a product: it is the factory for the second
The first product must also leave standards, contracts, pipelines and learnings that reduce the cost and risk of the next one.
Article 24Career & EntrepreneurshipBusiness and P1–P6
Solo founder + AI squad: speed without outsourcing judgment
How a founder can orchestrate research, product, architecture and engineering with AI without outsourcing priority, risk or acceptance.
Article 42TechniqueArchitecture
IT does not disappear: it becomes the platform for democratizing AI
Minimum architecture to transform distributed AI experiments into identified, governed and operable assets without recreating the IT queue.
Article 43TechniqueSecurity and Governance
Three gates for production agents: boundary, stopping, and consensus
Evidence Brief on SSRF across redirects, stopping conditions for iterative repair, and the use of ensembles as a review queue rather than truth.
Article 23Career & EntrepreneurshipStart here
Taste, responsibility and clarity: the human skills that have become more valuable
When producing becomes cheaper, clarity, taste, critical thinking, responsibility and learning raise the standard of work.
Article 22TechniqueSecurity and Governance
Sandbox, capability and identity: task-based security in agentic systems
A security contract per task that separates workspace, sandbox, identity, capability, data, time, policy and evidence.
Article 41TechniqueSecurity and Governance
Localhost is not identity: controls for agent local APIs
Evidence Brief on identity, authorization, CORS, control planes, sandboxing, and receipts for local APIs used by AI agents.
Article 20Career & EntrepreneurshipStart here
Your new portfolio should prove how you think, verify, and learn
A professional case-study model that shows the problem, decisions, use of AI, verification, outcome, limits, and learning.
Article 40TechniqueSecurity and Governance
The repository is not trustworthy: controls for agents at the workspace–runtime boundary.
Evidence Brief on monotonic configuration, approval, confinement, environment, and network for agents operating on untrusted code.
Article 39TechniqueQuality, Evals and Contracts
Formal proof and verifiable outputs: when the verifier settles the question
An Evidence Brief on proof-carrying outputs, independent verifiers, and what the formalization of Fermat teaches agent systems.
Article 38TechniqueSecurity and Governance
Agent Control Standard: how to prove controls on runtime without calling warranty preview
A technical reading of the OWASP ACS preview v0.1, with integration architecture, minimum tests, limits of evidence and adoption criteria.
Article 37TechniqueQuality, Evals and Contracts
AI-Native Product Lead: the contract between intent, agents and evidence
A verifiable design to connect value, architecture, evals, risk, economy and human authority in products built with AI.
Article 35TechniqueQuality, Evals and Contracts
It passed the tests. Is it ready to operate?
A proposed protocol to distinguish functional correctness, behavior under load and recovery capability in services built with AI assistance.
Article 33TechniqueQuality, Evals and Contracts
AI writes, AI reviews: independence needs to be demonstrated
The review study IA-for-IA converted into authority segregation, protected checks, human approval and release provenance.
Article 32TechniqueSecurity and Governance
Shadow builders: controls and evidence to operate AI-Native software
The RedAccess case converted into three model, discovery, negative tests and Evidence Gate for applications quickly created with AI.
Article 13Career & EntrepreneurshipBusiness and P1–P6
AI lowers the cost of experimenting; it does not validate your hypothesis
How to use AI to prepare better experiments without promoting a synthetic persona, click, prototype, or opinion into market evidence.
Article 12Career & EntrepreneurshipBusiness and P1–P6
From idea to first revenue: P1–P6 for AI-first founders
A six-phase path to turn building speed into evidence of a problem, product, operation, and a real exchange of value.
Article 11TechniqueQuality, Evals and Contracts
The agent cannot be its own auditor
How to separate executor, checker, reviewer and approver to use AI in evaluation without creating circular authority or fictitious independence.
Article 31TechniqueEvidence
Fable, Sol, and Terra: what really changed for builders
A dated radar of new AI portfolios and a method for choosing by task, risk, cost per outcome, evals, and verifiability.
Article 10TechniqueEvidence
Evidence before adjective: how to defend claims about AI systems
A practical grammar to link claims to subject, property, mechanism, evidence, verifier, validity and limit of inference.
Article 09BusinessSecurity and Governance
Autonomy proportional to risk: where to delegate to AI and where to keep the human gate
A method for grading agent authority by effect, reversibility, sensitivity and uncertainty — without confusing capacity with permission.
Article 08TechniqueQuality, Evals and Contracts
Spec-driven without self-deception: coverage, contract, eval and OpenAPI are not the same proof
How to separate specification, contract, test, coverage and eval so that each green supports only the decision it actually verified.
Article 07BusinessBusiness and P1–P6
From Pilot to Value: Measure Accepted Change, Not Produced Tokens
A practical contract to measure AI pilots by accepted outcomes, quality, flow, total cost, and risk — not by tokens or artifacts.
Article 06Techniqueengineering harness
Engineering harness in practice: six plans to transform intention into evidence
A technical guide for organizing intent, policy, execution, verification, evidence, and feedback around AI agents.
Article 05BusinessBusiness and P1–P6
AI amplifies the business you already have
AI does not transform an organization alone: it enhances clarity, flow and learning — or accelerates ambiguities, queues and rework.
Article 04BusinessWhy FORGE
Why Trustyu created FORGE: from a founder's memory to a verifiable system
JARVIS's journey to FORGE and the six changes that transformed AI memory, decisions, and execution into a verifiable system.
Article 03BusinessBusiness and P1–P6
Accountable human, executing AI: the contract that organizes the squad
AI can perform more work without receiving accountability: clear roles, boundaries, verifiers, and evidence preserve accountability.
Article 02Techniqueengineering harness
The model is not the system: why powerful AI still needs a harness
Models change quickly; reliable results depend on context, tools, policies, verification, evidence and learning.
Article 01Career & EntrepreneurshipStart here
After the pyramid: what changes at work when AI performs more
AI amplifies execution, but purpose, learning, judgment and accountability depend on how we redesign work.