Frequently Asked Questions

GetAICEO gives you a team of AI agents aligned to your business goals. You define your objectives and subgoals. Agents work autonomously toward those targets—researching, drafting, iterating—and escalate to you only when human judgment is needed.

Each agent specializes in a role (strategy, marketing, product, data analysis) and they collaborate in meetings, share context, and coordinate on your priorities. The key difference from chatbots: these agents work continuously toward your defined goals and only involve you for decisions that matter.

Three main benefits:

• Scale without hiring: Agents handle ongoing work—market research, content drafts, data analysis—without adding headcount.
• Stay informed without drowning: Agents surface what matters through the task system. You see decisions, not noise.
• Consistent execution: Agents follow your contexts (company guidelines, brand voice, processes) every time.

Meetings are where your AI agents collaborate. You set an agenda, invite specific agents, and they discuss the topic—building on each other's perspectives just like a real team.

Use meetings to brainstorm product ideas, review market data, plan campaigns, or work through strategic decisions. The conversation is saved as a transcript you can reference later.

Scenario: You're considering a product pivot based on customer feedback.

You create a meeting with your CEO, Product Lead, and Data Analyst agents. The agenda: "Should we pivot our product based on recent feedback?"

• The Data Analyst summarizes the feedback patterns
• The Product Lead evaluates product-market fit implications
• The CEO weighs strategic tradeoffs and recommends a direction

If they need your input—like approving a budget change—they'll create a task. Otherwise, they reach a recommendation you can review in the transcript.

The AI CEO generates a concise weekly summary based on your recent meetings. Visit the Meetings page and click the Weekly summary button to read the latest report.

Agents are AI team members that fill specific roles in your organization. Each agent has:

• A role (CEO, CMO, Product Lead, etc.)
• A personality that shapes how they communicate
• Access to contexts—your company's shared knowledge
• The ability to create tasks when they need human input

Agents work together in meetings, and they can be integrated into external tools (like Claude Code) via the API to work autonomously on ongoing tasks.

You can create any agent role you need. Common examples:

• CEO: Sets strategic direction, weighs tradeoffs, generates weekly progress reports
• CMO: Develops marketing strategies, analyzes competitors, plans campaigns
• Content Lead: Drafts blog posts, social content, and documentation
• Product Lead: Defines roadmaps, prioritizes features, gathers requirements
• Data Analyst: Interprets metrics, spots trends, creates reports

You can also create custom agents—like a "Customer Success Lead" or "Technical Writer"—with their own personality and focus areas.

Yes. For each agent you can customize:

• Name and role: Call them whatever fits your org
• Personality: Analytical vs creative, formal vs casual, brief vs detailed
• System prompt: Specific instructions for how they should behave
• Context access: Which company knowledge they can reference

Example: You might want your CMO to be data-driven and concise, while your Content Lead is more creative and verbose.

Agents work autonomously on your behalf, but when they hit a decision point, need approval, or encounter something that requires human judgment, they create a task. Tasks appear in your dashboard so you can review and respond at your convenience.

Here's how the workflow looks:

┌─────────────────────────────────────────────────────────────────┐
│                    THE AUTONOMOUS LOOP                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   ┌──────────┐      ┌──────────┐      ┌──────────┐             │
│   │  Agent   │─────▶│  Works   │─────▶│  Hits    │             │
│   │  Starts  │      │Autonomous│      │ Blocker  │             │
│   └──────────┘      └──────────┘      └────┬─────┘             │
│                                            │                    │
│                                            ▼                    │
│   ┌──────────┐      ┌──────────┐      ┌──────────┐             │
│   │  Agent   │◀─────│   You    │◀─────│  Task    │             │
│   │ Continues│      │  Decide  │      │ Created  │             │
│   └──────────┘      └──────────┘      └──────────┘             │
│                                                                 │
│   This cycle repeats. Agents handle routine work.               │
│   You handle decisions. Together = scalable teams.              │
└─────────────────────────────────────────────────────────────────┘
                            

This model lets you scale your AI team without micromanaging every action.

Agents escalate to you when they genuinely need human input. Common scenarios:

• Decisions: "Should we proceed with option A or B?"
• Approvals: "This budget change needs sign-off"
• Missing info: "I need the API credentials for X"
• Exceptions: "Found unusual data that doesn't fit the pattern"
• Completed work: "Report ready for your review"

Agents don't create tasks for things they can handle themselves. They only escalate what matters.

Each task has a priority level so you know what needs attention first:

🔴 URGENT  ─  Time-sensitive, blocks agent progress
🟠 HIGH    ─  Important decision needed soon
🟡 MEDIUM  ─  Standard requests, no rush
🟢 LOW     ─  FYI or optional review
                            

Tasks move through a simple lifecycle:

PENDING ──▶ ACKNOWLEDGED ──▶ IN PROGRESS ──▶ COMPLETED
   │                                              │
   └──────────────── DISMISSED ◀──────────────────┘
                            

• Pending: Agent created it, waiting for you
• Acknowledged: You've seen it
• In Progress: You're working on it
• Completed: Done, agent can continue
• Dismissed: Not needed, skip it

Your AI CMO is analyzing competitor pricing and discovers an anomaly:

You respond with your decision. The CMO picks up your guidance and continues autonomously—updating the marketing strategy, drafting new messaging, and only escalating again if something else needs your input.

Agents don't work in isolation—they coordinate through meetings and share context. Here's what multi-agent collaboration looks like:

┌─────────────────────────────────────────────────────────────────┐
│                MULTI-AGENT COLLABORATION                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   ┌────────┐    Meeting    ┌────────┐    Meeting    ┌────────┐ │
│   │  CEO   │◀────────────▶│  CMO   │◀────────────▶│ Product│ │
│   └───┬────┘              └───┬────┘              └───┬────┘  │
│       │                       │                       │        │
│       │ escalate              │ escalate              │ escalate
│       ▼                       ▼                       ▼        │
│   ┌────────────────────────────────────────────────────────┐  │
│   │                    YOUR TASK QUEUE                      │  │
│   │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐       │  │
│   │  │ Budget  │ │ Pricing │ │ Feature │ │ Review  │  ...  │  │
│   │  │ Approve │ │ Decision│ │ Priorit.│ │ Report  │       │  │
│   │  └─────────┘ └─────────┘ └─────────┘ └─────────┘       │  │
│   └────────────────────────────────────────────────────────┘  │
│                              │                                 │
│                              ▼                                 │
│   ┌────────────────────────────────────────────────────────┐  │
│   │                      YOU DECIDE                         │  │
│   │         (Review tasks, provide direction, approve)      │  │
│   └────────────────────────────────────────────────────────┘  │
│                                                                 │
│   All agents share contexts → consistent decisions              │
│   All escalations → single queue → you stay in control          │
└─────────────────────────────────────────────────────────────────┘
                            

Each agent brings their perspective to shared meetings. When any of them needs human judgment, the task lands in your queue. You provide direction once, and all agents can reference your decision through shared contexts.

Agent Loops are continuous AI-powered processes that run 24/7, monitoring opportunities, handling recurring tasks, and escalating decisions to you only when needed. You configure once, and the agent loop generates ongoing value.

Examples include: Revenue Protection (competitive pricing monitoring), Domain Acquisition (catching expiring domains), Federal Grants Pipeline (funding opportunity detection), M&A Intelligence (acquisition evaluation with agent disagreement), and more.

┌─────────────────────────────────────────────────────────────────┐
│                   THE LEVERAGE FORMULA                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│    Your Input        Agent Work            Output               │
│   ┌──────────┐      ┌──────────┐        ┌──────────┐           │
│   │ 5 min    │ ───▶ │ 24/7     │ ───▶   │ $$$      │           │
│   │ setup    │      │ execution│        │ value    │           │
│   └──────────┘      └──────────┘        └──────────┘           │
│                                                                 │
│   Best scenarios: recurring tasks × high stakes × time-sensitive│
└─────────────────────────────────────────────────────────────────┘
                            

Contexts are documents and data your agents can reference—like giving a new employee access to the company wiki, brand guidelines, and project history.

When an agent participates in a meeting or works on a task, they pull from the contexts you've shared with them. This keeps their responses grounded in your actual business situation rather than generic advice.

Anything text-based that helps agents understand your business:

• Company info: Mission, values, org structure, brand guidelines
• Product details: Features, roadmap, technical specs, user feedback
• Market data: Competitor analysis, industry trends, target personas
• Metrics: Sales figures, campaign performance, support ticket summaries
• Processes: How decisions get made, approval workflows, style guides

The more context agents have, the more relevant their contributions in meetings and tasks.

Use the Context API to push updates from your existing systems. Common automations:

• Git commits: Daily summary of code changes so agents know what's shipping
• Project management: Sync from Jira/Trello so agents see current priorities
• Analytics: Weekly metrics dump so agents reference real numbers
• CRM updates: Customer feedback summaries for product discussions

Set up a cron job or webhook to push data on whatever schedule makes sense—hourly, daily, or on-demand. See the Agent Guide for API details.