Welcome. This is the executive version of Arvantti Harness®, designed for senior leadership. We will not talk about code: we will talk about how to put artificial intelligence to work for the company without losing control. The core idea: useful agents, built to fit, protected by guardians, with the company's memory, and with a record of everything they do. Arvantti Harness® is a registered trademark of Arvantti Labs, a brand of Sequoia Space.

Four pains any board recognizes. One: AI came in through the back door; every team has its own tool and there is no common policy. Two: AI does not know the company, so it answers with generic or wrong information. Three: AI costs grow fast because every conversation resends everything before it. Four: there is no traceability, which is a legal, reputational, and operational risk.

Arvantti Harness® in one sentence: it is the control room for the company's AI. Four verbs sum up what it does: create agents, protect them, give them memory, and be accountable. A key point for the board: there is no need to switch AI providers. Arvantti works as a harness of harnesses: it wraps the tools the team already uses (Claude, ChatGPT/Codex, Gemini, Copilot, local models, among others) and puts them under one policy, one memory, and one record.

This is one of the most important messages for the board. Arvantti Harness® is a harness of harnesses: an enterprise control layer that sits above the AI tools. Inside it, each team can work with the AI it prefers: Claude, ChatGPT and Codex, Gemini, OpenCode, OpenRouter (which gives access to hundreds of models), Microsoft Copilot, Antigravity, Kimi, Pi, or local and private models running on the company's infrastructure. All of them fall under the same policies, the same guardians, the same enterprise brain, and the same audit trail. Each tool keeps its own capabilities; Arvantti does not limit them, it governs them. Benefit: the company can switch providers, combine several, or use the best model for each task without rebuilding agents, rules, or memory, and without being locked into any provider.

This is the commercial core. Creating an agent is not a software project: it is describing a process. The visual designer lets you draw the steps; the catalog brings more than 120 reusable skills, grouped into business families (marketing, security, cost control, finance, planning, IT, web design). Each agent has a role (for example: architect, data analyst, reviewer, deployment monitor), a clear scope, and an accountable person. Agents can also have scheduled work: a weekly project review or a monthly dependency review, always with human approval for actions that have an effect.

An agent's life cycle looks like a new employee's: you define the role, give it tools, teach it the process, test it, and authorize it. Every version is saved, with who created it, which AI model it used, and what changed; if a new version does not work, you go back to the previous one. Self-improvement works like this: observe, propose, test in a controlled environment with a budget, human review, explicit promotion, and the option to roll back. Guardians, credentials, policies, and permissions can never be edited by the agents themselves.

Screenshots of Arvantti Shell. On the left, agent packs: for example an executive pack with technology, operations, marketing, and finance agents that are switched on or off per project. On the right, the agent network: a map showing which agent uses which skills and flows, so leadership can see its digital workforce at a glance.

One of the most striking capabilities for an executive: talking to Arvantti by voice. You speak to the coordinator as you would to a project manager. The agent coordinator decides which agents take part and splits the work; the thread coordinator opens a line of work for each front, follows it in parallel, and reports back. You receive progress, results, and pending approvals, also by voice, and you can approve or ask for changes on the spot. Everything requested and done by voice goes into the audit log, under the same rules, guardians, and permissions as the rest of the platform. Benefit: leadership can delegate and follow up from anywhere, with no technical knowledge.

Principle: deny by default. An agent cannot do anything it has not been authorized to do. Guardians are like the building's security staff: they check every action before it happens; they block access to sensitive folders, replace any key or password that appears with the word [REDACTED], and control spending (there are cost guardians for AI inference, databases, external APIs, storage, and retries). Hooks are automatic checkpoints that fire at defined moments, for example an audit before every change. Permissions are granted per person or agent, per resource, and per operation, and they can be revoked. Example for the board: if an agent tries to read an access key, the guardian blocks it and asks for authorization; if a key shows up in a result, it is masked before it reaches any log.

The enterprise brain is the organization's memory. It stores what is known, what was decided, and what happened, always with its source and how current it is. Graph RAG in plain words: the AI does not just look for text similar to the question (that is a search engine); it also walks a map of relationships between people, customers, orders, decisions, and documents. That is why it can answer questions like why did we change our resin supplier? by citing the decision, who made it, when, and with what evidence. There are three brains: the project brain (plans, tasks, and documents), the decision graph (who decided what, when, and with what evidence, including pending decisions), and the systems or code map for technical teams. Agents never touch the database directly: they query through controlled channels. Everything can live on the company's own hardware, in a local cloud, or in its private cloud.

Self-managed means the brain does not depend on someone feeding it by hand. Synchronization (Brain Sync) automatically records confirmed decisions, task changes, verification results, progress, and closures for each project. The brain flags how current the information is, keeps snapshots for restoring, and is evaluated with sets of test questions that measure coverage, retrieval, and citation quality. It integrates with Obsidian-style note vaults that many teams already use. Message for the board: knowledge becomes an asset of the company, not of individuals.

Context is everything the AI has in view while it works. Too little makes it get things wrong; too much distracts it and makes every query more expensive, because models charge for everything they read on each turn. Arvantti builds each agent's context to fit: a short catalog of what is available, and the detail only when it is requested. Each task has a context budget; if something does not fit, it is paged, summarized, or rejected, but never silently cut. Each piece of information carries a fingerprint (a digest) that proves it is the same one that was approved. And each agent works in its own space, so agents do not contaminate one another.

Every action by a person or an agent is answered with four questions: who (a person, an agent, a flow, or the system, each with its own identity), what (the action and the fingerprint of the before and after), with what (the tool and the declared or reported AI model, or a not-available mark when it is unknown; it is never made up), and when (date and time in UTC). The screenshot shows the activity log: for example, an architect agent that updated a project decision using a declared model, or a data agent that updated a record.

The audit log is the black box of the company's AI. Every effect leaves a receipt with a unique identifier, actor, action, and time. It covers what traditional version control does not: design documents, plans, brain decisions, database records, changes to agents, flows, hooks, and connectors, and sign-ins. It is append-only, stores fingerprints of the before and after instead of copies of the content, and never stores keys. If a piece of data is not available, for example which model an external agent used, it records it as not available instead of making it up. Benefits: investigate incidents in minutes, answer auditors and regulators, and give the board confidence. On ISO/IEC 42001: the audit log is a central part of the evidence the standard requires; certification is granted by a certification body.

Just as the company keeps a record for each employee, Arvantti keeps one for each agent. Every flow, checkpoint (hook), skill, or connector has a current version, a previous version, a scope (global, project, or local), an owner, a declared AI model, and a fingerprint that proves it did not change without a record. Proposed improvements stay as candidates under review; nothing is promoted automatically. And if something is concerning, a connector or agent is disabled immediately.

AI governance can be summed up in six questions that any board or risk committee should be able to answer. Arvantti answers each one with a concrete function: an inventory of agents and versions; shared per-project policies with local restrictions (local rules can only restrict, never open up); specific, revocable permissions with the reason for every denial; mandatory human approval to publish, delete, connect to external services, or touch secrets; cost guardians; and the audit log. ISO/IEC 42001 is the international standard for artificial intelligence management systems: it requires an inventory of AI systems, roles and responsibilities, risk assessment, controls, traceability, and continuous improvement. Arvantti generates that evidence automatically (agent inventory and versions, policies and permissions, human approvals, audit log), which makes it easier to prepare for certification. It also supports information security (ISO/IEC 27001) and document management audits. Arvantti does not issue the certification: a certification body grants it. In addition, work triage: every request is assessed and, depending on its risk, done directly, as a task, or with a formal plan.

This slide is a glossary that translates for leadership what the technical team will mention. Harness of harnesses and adapters: Arvantti wraps Claude Code, Codex, Gemini, Copilot, OpenCode, OpenRouter, Kimi, Pi, or local models, without replacing them. Work intake and SDD (specification-driven design): every request is assessed and the recommendation is to do it directly, as a task, with a light plan, or with a full seven-phase plan; the final decision is human. Tasks and receipts: traceable tasks with a receipt. Terminals and sessions: each agent works in its own real console and the board shows its status. Worktrees: separate working copies for parallel work. Council room: a space where agents weigh proposals before acting, with a person as the referee. MCP and connectors: integration with external systems; credentials live in Arvantti, never in the agent, and connecting outside requires human consent.

Six benefits to close the case. Speed: agents are built from catalog skills. Control: deny by default and human approval for anything sensitive. Compliance: the audit log produces evidence with no extra work. Predictable cost: context budgets and cost guardians. Your own knowledge: the enterprise brain lives on the company's infrastructure. Provider freedom: because Arvantti wraps the AI tools, you can switch models or providers without rebuilding agents or rules.

A four-step starting proposal. One, assessment: an inventory of where AI is already used, with which tools, and which process has the most potential. Two, pilot: one or two agents in a real process, with guardians, permissions, and an audit log from day one. Three, measurement: compare time, cost, and quality before and after. Four, scale to more processes and teams on the same rules. The screenshot shows the home screen: what should move forward today, the next step, agents with changes under review, and traceability events.

A capabilities summary to leave on screen during the conversation. Agents: voice conversation with the agent and thread coordinators, visual designer, a catalog of more than 120 skills, roles with owners, versions with rollback, and scheduled work. Security: guardians, hooks or checkpoints, revocable permissions, masked keys, and human approval for anything sensitive. Brain: enterprise memory, graph RAG with citations, self-management, and tailored context, with data on the customer's infrastructure. Governance: an audit log with who, what, with what, and when, per-project policies, cost guardians, and an inventory of the company's AI. Also: works with any AI (Claude, Codex, Gemini, OpenCode, OpenRouter, and local models), several agents in parallel, planning based on each request's risk, a council room, connectors to systems such as CRM and ERP, a receipt for every task, and evidence ready for ISO/IEC 42001 certification.

Closing: the invitation is to choose a concrete process for the pilot. Propose a short assessment and a demo with the customer's team. Contact: Andrés Quiceno, CTO of Engineering. Web: arvantti.tech. LinkedIn: Arvantti Labs and Sequoia Space Latam. Arvantti Harness® is a registered trademark of Arvantti Labs, a brand of Sequoia Space.