Vinicius Apolinario  de Brazil Inspired when he saw a 1 gigabyte hard drive Jumpers on hardware to change settings Virtual servers VMware, virtual box Cloud Containers > kubernetes AI > MCP > agents > claws > All new technologies are disruptive The job itself did no disappear The abstraction moves one layer up AI is moving faster and faster The task that the AI agent is going to do already exists AI agents have goals Users are going to put a lot of stuff in production that operators do not necessarily trust What are the opportunities ? What are the responsibilities ? Reduce repetitive work Reduce the stress of problem-solving Improve the processes  This all needs adult supervision! Agents can act with incomplete context They go off in an odd direction… Your AI agent can move quickly in the wrong direction The formula: Context - what really matters Boundaries - what is allowed and the limits Feedback - whether the agent did something right or wrong Accountability - if an AI is doing a process, someone is held responsible Do not automate a process unless you understand it! What problem are we solving? Who experiences the pain? What data matters? What does “good” look like? What happens if it is wrong? Should this be automated at all? Strategic thinking should probably have a human involved Lawyers do not know the answers to the questions, which are nuanced Boundaries Identity Permissions Tool access Data access - does the agent have its own access or that of a human? Approval flows Network boundaries Secrets - who is asking for the data? Cost limits (this is an specific AI problem for subscriptions) suggest (low risk) > draft (human reviews) > execute with approval (human authorizes) > execute within limits (guardrails + monitoring) > autonomous (strong controls + rollback) /whatif explores without executing Feedback traditional signals: latency, errors, dependencies, cost, availability  Agent signals: goal, prompt/context, retrieved data, tool calls, evaluation result Human signalrs User feedback, overrides, approvals, excavations, business outcomes If you cannot observe, you cannot improve. Accountability Who approved the workflow? Who owns the data? Who owns the tools? Who responds when it fails? Who decides the autonomy level? Who explains the outcome? How is a system behaving on behalf of people? A person would have accountability. An agent needs this ,too. Developed debate: The impact of coding agents on developers Junior developers do not have the experience to understand if generated code is the best If the code executes what I want, isn’t that okay The value of the developer is in understanding the value of the code, not in writing it. Infrastructure: AI is just another workload. Access. Logs. Cost. Events. Typical and known issues. What is the blast radius when something goes wrong? Backups? Restoration? Data: Context is key. Accessing the data is not the same as understanding the business. What is the business goal? Business Leader: Pressure comes from shareholders > leaders > managers > workers “Do AI” Implement AI to make it better! AI should not necessarily change the process. When are decisions slow? Where can we improve the process? What work is repetitive? What are the low-hanging fruits? What needs to be studied? Homework Build one small agent Connect it to one real workflow Add one meaningful boundary Measure one outcome Prioduction 1. Pick one painful 2. Indentify safe agent assistance 3. …