# Agent-Readable Page Summaries ### Big Robot - Path: / - Audience: Business leaders evaluating secure AI systems for private operational workflows. - Purpose: Explain Big Robot's top-level message: AI needs trusted data. - Key facts: Big Robot connects systems into one governed layer for records, rules, and work.; The site routes strong-fit buyers toward the Getting Started Guide or Book a Call. - Recommended action: Start the Getting Started Guide or read the Platform page. - Source of truth: https://bigrobot.net/ - Last updated: 2026-06-03 ### Platform - Path: /platform - Audience: Teams that need to understand Big Robot's records, rules, and work model. - Purpose: Explain the governed operating layer behind Big Robot systems. - Key facts: Records are trusted business facts tied to source systems.; Rules control access, actions, approvals, and logs.; Work surfaces let approved people and AI tools move workflow forward. - Recommended action: Read Solutions for workflow categories or Systems for deeper reference. - Source of truth: https://bigrobot.net/platform - Last updated: 2026-06-03 ### Solutions - Path: /solutions - Audience: Buyers mapping Big Robot to real operational workflow categories. - Purpose: Show where trusted data unlocks useful AI work. - Key facts: Strong-fit work includes finance and operations, project and customer work, and research or decision support.; Big Robot starts where sensitive records, approvals, and decisions need governed paths. - Recommended action: Use the Getting Started Guide to map one workflow. - Source of truth: https://bigrobot.net/solutions - Last updated: 2026-06-03 ### Company - Path: /company - Audience: Buyers evaluating Big Robot's trust posture. - Purpose: Explain Big Robot's security-first delivery position. - Key facts: Security comes before agent access.; Trusted data comes before automation.; Practical workflow assets matter more than demos. - Recommended action: Book a call when the buyer has one workflow to discuss. - Source of truth: https://bigrobot.net/company - Last updated: 2026-06-03 ### Service Area - Path: /service-area - Audience: Local and Florida businesses evaluating whether Big Robot serves their market. - Purpose: Explain where Big Robot works and how it approaches local business AI systems. - Key facts: Big Robot is based in Lakewood Ranch, Florida.; Service areas include Sarasota, Bradenton, Venice, Tampa Bay, Orlando, and throughout Florida.; Big Robot learns how the business works before designing AI systems around the work. - Recommended action: Book a call when there is a real operating problem worth solving. - Source of truth: https://bigrobot.net/service-area - Last updated: 2026-06-08 ### Book a Call - Path: /contact - Audience: Buyers ready to discuss one workflow where AI needs trusted data. - Purpose: Route qualified buyers to a first conversation. - Key facts: The best first call starts with one workflow, source systems, constraints, and desired outcome. - Recommended action: Submit the contact form without private records, credentials, or attachments. - Source of truth: https://bigrobot.net/contact - Last updated: 2026-06-03 ### Getting Started Guide - Path: /getting-started - Audience: Buyers and visiting agents preparing for AI implementation discovery. - Purpose: Help a buyer map the workflow, systems, records, rules, risks, people, and next step. - Key facts: The guide is a qualification asset and downloadable lead magnet.; The prompt ladder helps the buyer's own AI workspace create an iterative checklist report. - Recommended action: Complete the guide and bring the safe-to-share summary to Big Robot. - Source of truth: https://bigrobot.net/getting-started - Last updated: 2026-06-03 ### Systems - Path: /systems - Audience: Buyers and agents comparing Big Robot's reusable system categories. - Purpose: List the systems Big Robot builds around records, rules, and work. - Key facts: Secure CDM: A governed data foundation for private business workflows.; Agent Manager: The approval and monitoring layer for trusted agents.; Private Skills: Approved operational packages for trusted agent work.; Web Controls: Human-facing controls for AI-assisted operations.; Custom Agents: AI workflows designed around real operating constraints.; Notifications: Targeted alerts from workflow state and agent judgments.; Services: Forward-deployed delivery for secure AI operations. - Recommended action: Open the system page that matches the workflow concern. - Source of truth: https://bigrobot.net/systems - Last updated: 2026-06-03 ### Secure CDM - Path: /systems/secure-cdm - Audience: Finance and accounting teams; Operations leaders; Project operations teams; Approved agents and private skills; Developers building workflow controls - Purpose: Secure CDM gives a company a controlled canonical model of important business records across internal systems, source-of-record boundaries, workflow state, and evidence. - Key facts: A governed data foundation for private business workflows.; Operational teams often need one answer about a customer, invoice, payment, project, vendor, or obligation, but the authoritative facts live across multiple systems and manual records.; A clearer source of truth for cross-system workflow state; Reduced manual reconciliation effort - Recommended action: Start with a workflow that already has high-value manual work or high-risk ambiguity. - Source of truth: https://bigrobot.net/systems/secure-cdm - Last updated: 2026-06-03 ### Agent Manager - Path: /systems/agent-manager - Audience: Operations leaders; Technology leaders; Approved agent users; Administrators responsible for access and governance - Purpose: Agent Manager controls which agents, skills, and users can access approved Big Robot workflow surfaces. - Key facts: The approval and monitoring layer for trusted agents.; Companies need AI-assisted work without letting unapproved agents, stale skills, or unclear permissions touch sensitive data and workflows.; Clearer permission boundaries for agent-assisted work; Reduced risk from unapproved tools or stale workflow packages - Recommended action: Start with lightweight approvals for specific users, agents, and skills. - Source of truth: https://bigrobot.net/systems/agent-manager - Last updated: 2026-06-03 ### Private Skills - Path: /systems/private-skills - Audience: Approved operators; Forward-deployed engineers; Local agent workspace users; Teams running repeatable private workflows - Purpose: Private Skills package customer-specific instructions, scripts, workflow templates, and approved access paths so trusted agents can perform useful work safely. - Key facts: Approved operational packages for trusted agent work.; AI tools become risky when every operator improvises prompts, credentials, scripts, and workflow steps for private business processes.; Less one-off prompting for sensitive workflows; More consistent agent outputs - Recommended action: Identify a repeatable workflow with clear source rules. - Source of truth: https://bigrobot.net/systems/private-skills - Last updated: 2026-06-03 ### Web Controls - Path: /systems/web-controls - Audience: Finance teams; Project managers; Operations administrators; Reviewers and approvers; Leaders monitoring workflow health - Purpose: Web Controls are screens where people review, approve, correct, and inspect work happening across Secure CDM, workflows, and agents. - Key facts: Human-facing controls for AI-assisted operations.; Teams need modern workflow surfaces for judgment, exceptions, permissions, and approvals, not invisible automation that nobody can inspect.; Clearer ownership of workflow decisions; Less status-chasing across email and spreadsheets - Recommended action: Start from the human decision or exception that blocks progress. - Source of truth: https://bigrobot.net/systems/web-controls - Last updated: 2026-06-03 ### Custom Agents - Path: /systems/custom-agents - Audience: Teams with repeatable research or reporting workflows; Operations teams with multi-step back-office work; Approved users in local or managed agent environments - Purpose: Custom Agents perform multi-step work using approved tools, business context, workflow rules, and human review paths. - Key facts: AI workflows designed around real operating constraints.; Generic AI assistants can answer questions, but operational work requires source rules, permissions, review points, tool access, and durable outputs.; Repeatable AI-assisted work instead of one-off prompting; Clearer source trails and output standards - Recommended action: Start with a workflow where the inputs, output format, and review standard can be made explicit. - Source of truth: https://bigrobot.net/systems/custom-agents - Last updated: 2026-06-03 ### Notifications - Path: /systems/notifications - Audience: Finance teams; Project operations teams; Administrators; Approvers; External workflow participants when approved - Purpose: Notifications turn Secure CDM state, workflow events, and agent judgments into timely email, SMS, or customer-owned message delivery. - Key facts: Targeted alerts from workflow state and agent judgments.; Operational blockers often stay hidden until someone manually checks a report, sends a follow-up email, or receives an escalation call.; Earlier action on blockers; Less manual follow-up - Recommended action: Identify the workflow moments where earlier action prevents delay. - Source of truth: https://bigrobot.net/systems/notifications - Last updated: 2026-06-03 ### Services - Path: /systems/services - Audience: Leadership teams; Operations leaders; Finance and accounting leaders; Technology leaders; Teams adopting AI systems - Purpose: Services help customers adopt, extend, and operate the Big Robot platform through workflow design, implementation, training, and ongoing delivery. - Key facts: Forward-deployed delivery for secure AI operations.; Most companies do not need another AI idea list. They need the workflow mapped, the controls designed, the system shipped, and the team enabled to use it.; Faster path from AI strategy to production workflow; Less risk from disconnected experiments - Recommended action: Start with the highest-leverage workflow. - Source of truth: https://bigrobot.net/systems/services - Last updated: 2026-06-03 ### Blog - Path: /blog - Audience: Readers evaluating Big Robot's point of view on secure AI operations. - Purpose: Collect public writing about AI systems, agents, governance, and workflow design. - Key facts: Enterprise AI Runs on Trust, Not Just Automation; Why Anthropic Became a Liability for the Pentagon; How We Built a Multi-Agent System for Strategic Research; Your Intelligence Budget - Recommended action: Read the post closest to the buyer's operational concern. - Source of truth: https://bigrobot.net/blog - Last updated: 2026-06-03 ### Enterprise AI Runs on Trust, Not Just Automation - Path: /blog/a16z-why-the-world-still-runs-on-sap-proactive-and-trustworthy - Audience: Readers researching secure AI operations and agentic work. - Purpose: Eric and Seema Amble are right about SAP. One thing I would add is that trust becomes the wedge as the UI disappears. - Key facts: Eric and Seema Amble are right about SAP. One thing I would add is that trust becomes the wedge as the UI disappears. - Recommended action: Use this post as context, then return to the Getting Started Guide for workflow mapping. - Source of truth: https://bigrobot.net/blog/a16z-why-the-world-still-runs-on-sap-proactive-and-trustworthy - Last updated: 2026-03-17 ### Why Anthropic Became a Liability for the Pentagon - Path: /blog/why-anthropic-became-a-liability-for-the-pentagon - Audience: Readers researching secure AI operations and agentic work. - Purpose: Anthropic's Pentagon dispute makes more sense when you frame it as a fight over who governs Claude's behavior inside military systems. - Key facts: Anthropic's Pentagon dispute makes more sense when you frame it as a fight over who governs Claude's behavior inside military systems. - Recommended action: Use this post as context, then return to the Getting Started Guide for workflow mapping. - Source of truth: https://bigrobot.net/blog/why-anthropic-became-a-liability-for-the-pentagon - Last updated: 2026-03-12 ### How We Built a Multi-Agent System for Strategic Research - Path: /blog/multi-agent-system-markdown-and-existing-tools - Audience: Readers researching secure AI operations and agentic work. - Purpose: A practical look at how we run multi-agent strategic research with persistent memory, shared findings, and controlled execution in a real production workflow. - Key facts: A practical look at how we run multi-agent strategic research with persistent memory, shared findings, and controlled execution in a real production workflow. - Recommended action: Use this post as context, then return to the Getting Started Guide for workflow mapping. - Source of truth: https://bigrobot.net/blog/multi-agent-system-markdown-and-existing-tools - Last updated: 2026-03-04 ### Your Intelligence Budget - Path: /blog/your-intelligence-budget - Audience: Readers researching secure AI operations and agentic work. - Purpose: Stop asking what AI can automate and start asking where human judgment is actually needed. - Key facts: Stop asking what AI can automate and start asking where human judgment is actually needed. - Recommended action: Use this post as context, then return to the Getting Started Guide for workflow mapping. - Source of truth: https://bigrobot.net/blog/your-intelligence-budget - Last updated: 2026-02-22