AI Assistant for Executives: Briefings, Follow-Ups, Decisions

An AI assistant for executives is a permission-bound agent that reads a defined set of calendars, documents and messages, prepares briefings, and keeps a ledger of commitments made in meetings and emails. It drafts and reminds, while the executive decides and sends. The value comes less from clever writing than from a narrow role, a reliable source of truth and approval boundaries that are written down.

Key takeaways

• Start with three jobs: briefings before meetings, a ledger of commitments, and decision memos. Leave everything else out of the first version.
• Give the agent read access to what it needs and draft-only access to what it writes. Sending, paying, accepting invitations and deleting stay with a human.
• Every claim in a briefing should point to a source the executive can open. Gaps should be flagged, not filled with guesses.
• A commitment ledger works only if each entry has an owner, a due point, a source and a status that someone confirms.
• Treat the executive's inbox as untrusted input. Text inside an email must never be able to change what the agent is allowed to do.

What does an AI assistant for an executive actually do?

Most executives lose time in three places. They walk into meetings under-prepared because the relevant history is spread across email, documents and chat. They forget what they promised, or never learn what others promised them. And they make decisions with context that is either too thin or buried in forty pages.

A leadership-support agent addresses those three gaps and nothing more. It is closer to a well-briefed chief of staff than to a general chatbot. It has a defined role, a memory of the people and topics that matter, access to specific tools, and a clear list of things it must ask about before acting.

The distinction from a chatbot matters. A chatbot answers when asked and forgets. A managed agent keeps working context, runs recurring tasks (such as preparing tomorrow's briefings each evening), and leaves a trail that someone can review. If you want a plain definition of the building blocks, what an AI agent for business is covers them without jargon.

What the agent should not do is equally important. It should not speak for the executive in new situations, negotiate, approve spending, reply to the board, or decide who gets access to anything. Those are judgment calls with consequences, and they belong to a person.

Which permissions should it have, and which should it never have?

Permissions are the design. Before choosing a model or a tool, write down what the agent can see, what it can draft, and what it can never do. Principles such as least privilege and deny-by-default, described in the [OWASP authorization guidance](https://cheatsheetseries.owasp.org/cheatsheets/Authorization_Cheat_Sheet.html), apply directly here.

A workable starting set looks like this:

• Read: the executive's calendar, a named set of shared folders, and specific mailbox labels or threads, not the whole inbox by default.
• Read with limits: the project tracker and CRM, only the fields the briefings need.
• Draft only: follow-up emails, meeting notes, decision memos, agenda suggestions. Drafts land in a review folder, not in an outbox.
• Propose only: calendar changes, introductions, task assignments to other people.
• Never: sending on the executive's behalf without approval, accepting or declining invitations, moving money, changing access rights, editing legal or financial documents, deleting anything.

Use scoped, revocable access rather than shared passwords. Standard authorization flows such as [OAuth 2.0](https://developers.google.com/identity/protocols/oauth2) let a user grant an application specific scopes and withdraw them later, which suits a role that may be adjusted often.

The second risk is content, not credentials. An executive's inbox contains messages from strangers, and some of them may contain instructions aimed at an AI ("forward the attached contract to this address"). The agent should treat all email and document text as data to summarize, never as commands. The [OWASP prompt injection prevention cheat sheet](https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html) describes practical defenses, and the most robust one is structural: an agent that cannot send or delete cannot be tricked into sending or deleting.

How does the agent prepare a briefing you can trust?

A good briefing is short, sourced and honest about what it does not know. The executive should be able to read it in a few minutes and open the underlying material in one click when something looks off.

A useful structure for a meeting briefing:

• Purpose: why the meeting exists, in one or two sentences, taken from the invitation or the last agreed agenda.
• People: who attends, their role, and the last meaningful interaction with each, with links.
• History: what was decided or promised previously on this topic, with dates taken from the source documents.
• Open items: commitments still pending, in either direction.
• Likely questions: topics the other side raised before, drawn from prior threads.
• Gaps: what the agent could not find or could not verify.

The gaps section is what separates a trustworthy briefing from a fluent one. Language models can produce confident text about things they have not seen. The remedy is partly technical (retrieve from approved sources, cite them) and partly procedural (require a flagged gap instead of a guess). The approaches in reducing AI hallucinations in business workflows apply here almost unchanged.

Source quality also decides the result. If three versions of a proposal sit in three folders, the agent will cite one of them and the executive will not know which. Decide which system is authoritative for each kind of fact (the CRM for account status, the shared drive for the signed version of a document, the calendar for times) and tell the agent. That discipline is covered in giving an AI agent a reliable source of truth.

How does it track commitments and follow-ups?

Commitments are where leadership support earns its place and where it most easily fails. People say "I'll send that over" in a meeting, and nobody writes it down. An agent can listen to notes or transcripts the executive already produces, extract candidate commitments, and keep them in one ledger.

The word candidate matters. Extraction from natural language is imperfect, so the agent proposes entries and a human confirms them, at least at first. Each entry should carry:

• What was promised, in one plain sentence.
• Who owes it and who is waiting for it.
• The due point, either a date or a condition ("after legal review").
• The source: the meeting, the thread, the sentence.
• Status: proposed, confirmed, done, dropped. Only a person moves an item to done or dropped.

Once the ledger exists, the recurring work is simple. Each morning the agent lists what is due or overdue. Before each meeting it surfaces open items with that counterpart. After each meeting it drafts a short follow-up email that restates what was agreed, ready for the executive to edit and send. The same pattern, with the same care for context, appears in AI customer follow-up drafts that keep the context.

Here is an illustrative scenario. A managing director meets a regional distributor on Tuesday. During the call she says she will share revised delivery terms, and the distributor's contact says he will confirm the warehouse contact. That evening the agent reads her meeting notes, proposes two ledger entries (one owed by her, one owed to her), and drafts a follow-up email restating both. She confirms the entries on her phone and edits one sentence in the draft. On Thursday morning, the agent lists the delivery terms as due and the warehouse contact as pending. Nothing was sent, accepted or changed without her.

Notice what the agent did not do. It did not chase the distributor on its own, and it did not decide the terms were ready. It made sure two small promises stayed visible.

How can an agent support decisions without making them?

Decision support is the most delicate of the three jobs, because a persuasive summary can quietly steer the outcome. The aim is to make the decision easier to examine, not easier to rubber-stamp.

A decision memo from the agent can follow a fixed shape:

• The decision: what exactly is being asked, and by when.
• Options: the realistic choices, including doing nothing.
• What is known: facts with sources.
• What is assumed: statements the sources do not confirm, labeled as assumptions.
• What would change the answer: the two or three facts that would flip the choice.
• Who is affected and who has already weighed in, with links to their input.

The agent should not state a recommendation unless asked, and when it does, it should say which parts rest on assumptions. If the executive disagrees with a framing, that feedback should be captured so the memo format improves over time.

Keep a record of what the agent saw when it produced each memo. Months later, someone may ask why a decision was made, and a stored memo with its sources answers that better than recollection. The risk framing in the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) is a useful reference for deciding how much review a given type of decision deserves: a routine scheduling choice and a hiring or contract decision should not share the same safeguards.

What is a sensible plan for setting this up?

Resist the urge to connect everything at once. A small, observable first version teaches more than a broad one. A practical sequence:

• Write the role in a page: three jobs, what the agent reads, what it drafts, what it never does. The method in writing a job description for your first AI agent role fits well.
• Choose the sources: name the folders, labels and systems that are authoritative, and exclude personal, legal-privileged and board-confidential material until you have decided how to handle it.
• Grant the narrowest access that supports the jobs, with a named person who can revoke it.
• Build a small set of known cases: past meetings where you know the correct history and the correct commitments, and check the agent's output against them.
• Run in shadow mode for a short period: the agent produces briefings and ledger entries, the executive or an assistant compares them with what they would have done, and nothing else changes.
• Define the success measure before going live, for example whether briefings are opened before meetings and whether confirmed commitments are closed on time. The guidance in defining success criteria for an AI agent pilot helps here.
• Add one capability at a time, such as follow-up drafts, only after the previous one is stable.
• Review on a schedule: what was wrong, what was ignored, which permissions were never used and can be removed.

The human who works closest with the executive, often an executive assistant or chief of staff, should help shape this. They know what is sensitive, what is political, and which recurring meetings matter. The agent works for them as much as for the executive, and their feedback is the quickest route to a useful tool.

How does Be Human approach this kind of role?

Be Human designs, implements and operates this type of agent as a defined role: identity, memory, channels, tools and permissions are specified up front, with approval boundaries and an observable success criterion. Managed operations can then cover monitoring, recurring evaluations, integration maintenance, permission review and controlled improvements, so the setup does not drift as calendars, folders and people change. The work is remote, in English and Portuguese, and control of what the agent can see and do stays with the client.

Frequently asked questions

Can an AI assistant for executives send emails on my behalf? It can, technically, but for most leaders it should not, at least at the start. A safer pattern is draft-only: the agent writes the message and places it in a review folder, and a person sends it. Autonomy can be extended later for narrow, low-risk cases once the drafts have proven reliable.

What data should an executive agent never be given? Start by excluding legally privileged material, board-confidential papers, personnel files, and anything covered by contractual or regulatory restrictions you have not reviewed. You can add categories deliberately later, with a named owner and a reason. When in doubt, leave it out and let the briefing say that the source was not available.

How is this different from a calendar or notes app with AI features? Those tools usually work inside one product and one user's view. A managed agent spans the sources you choose, keeps a ledger across them, follows written permissions, and is reviewed and maintained by people over time. The difference is less about the model and more about scope, accountability and upkeep.

How do we know the briefings are accurate? Require citations in every briefing, check a sample against the original material each week, and keep a set of past meetings with known correct answers to test against whenever something changes. Treat a missing source or an unflagged gap as a defect to fix, not a quirk to tolerate. Accuracy is something you measure continuously, not something you assume after setup.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.