The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI.
When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption. However, the phase of simple queries quickly evolved into a sophisticated problem: our teams didn’t want just a chatbot, and instead started asking more and more for specialized digital colleagues.
As usage surged, our engineering and product teams were flooded with requests from across the business. Our Marketing, Finance, and HR teams weren't looking for generic AI advice but asking for agents with vertical-specific knowledge and the ability to autonomously move their unique workflows forward. They needed tools that understood the deep context of our business data and could act as a specialized teammate rather than a general-purpose bot.
We realized that for AI to truly transform an enterprise, it couldn’t remain a one-size-fits-all tool. We had to bridge the gap between "generic output" and "specialized action." The constant demand from our own subject matter experts to solve their most manual hurdles is what drove us to create a full custom agent platform, AgentSpot.
The Internal Challenge: "10 Agents for Every Team"
Instead of a standard closed beta, we issued a radical internal challenge: every department, regardless of their technical background, was tasked with building 10 agents to automate their most manual hurdles. We wanted to see if the people closest to the problems (our subject matter experts) could build their own solutions.
The response was a surge of cross-functional creativity that codified our "tribal knowledge" into functional tools:
HR: Developed agents to streamline specialized workflows like an HR onboarding agent that guides new hires through documentation and provisions tool access automatically, and a Performance Review Assistant that aggregates peer feedback, OKR progress and activity data into structured review drafts.
Finance: Created "Budget Guardians" that monitor departmental spend and alerts budget owners when thresholds are breached.
Sales: Built a Quote Generator Assistant that drafts customized pricing proposals from deal context, pricing tiers and rep notes.
Engineering: Has a Documentation Writer that auto-generates API docs, changelogs and runbooks from code diffs and PR descriptions.
Marketing: Focused on campaign performance and content creation, with a campaign analyzer that pulls campaign metrics from ad platforms and surfaces what is working or wasting spend.
By the end of the sprint, we had 80+ unique agents running internally. It was a collaborative build where every department helped shape the product they were excited to use.
Standing on the Data Plane: Our Competitive Edge
While generic AI tools promise revolution, business users often struggle with how to trust them or prompt them effectively. This is where AgentSpot differentiates itself from general-purpose bots through the data plane. Unlike managed agent platforms that often struggle with the "last mile" of data accuracy, AgentSpot enables fact-based actions by standing directly on ThoughtSpot’s rich semantic layer. By leveraging Spotter’s unmatched data analysis skills, our agents don't just guess based on unstructured context; they perform deep, accurate analysis on your structured business data.
Unlike "black box" AI, our agents are deterministic and transparent. We provide a process you can visually check and trust, ensuring that every insight is grounded in your company’s governed data models.
Furthermore, we’ve prioritized true self-service. AgentSpot is built so that any business user - not just engineers - can turn a simple problem statement into a directive, accurate prompt. This ease of use allows those closest to the business pain to create their own specialized agents without ever needing to become a "prompt engineer".
From Data to Outcome: Purpose-Built Agents, Apps and Workflows
AgentSpot moves beyond data and insights to true execution by turning your best playbooks into autonomous systems.
Purpose-Built Teammates: These are named agents with specific roles and the judgment to know when to ask before acting. They reason, plan, and act on live data while remembering your specific role and history to ensure every answer is relevant.
Data Apps: live, beautifully designed applications to present your work into a shareable artifact rather than a static file. Apps pull information from all connected sources: ThoughtSpot models, Salesforce, Jira, and more. It fetches fresh data on every load and enforces each viewer's permissions, so the right people always see the right data. But it is not constrained by the classic dashboard structure: newspaper-style briefs, card views, interactive boards, themed reports… Describe it, and AgentSpot will build and host it for you.
Autonomous Workflows: You describe the desired outcome, and AgentSpot builds the logic and handoffs with no coding required. These adaptive flows can be scheduled to run on any cadence (daily, weekly, etc.) ensuring repetitive tasks get done behind the scenes for you.
The Model Context Protocol (MCP): Every agent acts on what is true because AgentSpot plugs directly into the tools your business runs on (CRM, HCM, ERP, Slack, and more) via the MCP standard.
Enterprise Grade: Governed from Day One
We built AgentSpot to give teams freedom from shadow AI: easy enough that anyone can use it, powerful enough that they'll want to, and governed closely enough that you can let them.
Connector Governance: Admins maintain total authority over the ecosystem. They decide exactly which connectors are available to users, choosing which internal systems (like your CRM, HCM, or ERP) agents are permitted to access.
Sandboxed Execution: All code generated by agents runs in a secure, private container that cannot touch production systems.
Auditability: Every request, tool call, and model interaction is logged, providing a traceable execution history for compliance and audit logging.
A Force Multiplier for R&D
Building AgentSpot has fundamentally changed the roles within our R&D team. With AI accelerating research, requirements creation, and documentation by 5x to 10x, the lines between traditional functions have blurred. Our product design team has started building functional prototypes that engineers could bring into code immediately. PMs began contributing directly to internal tool codebases to increase iteration speed, skipping the "wait for a slot in the roadmap" phase of development. When everyone can dabble in the "other side", the conversation moves away from "is this possible?" to "is this the best solution?"
A Human-Centric Agent Launch
AgentSpot enables fact-based actions by standing on a rich data product with unparalleled skills in extracting insights from structured data. Our agents are grounded in data and transparent, providing a process you can visually check and trust. Unlike general-purpose bots, our platform is an expert at turning a business user’s problem statement into a directive, accurate prompt without requiring them to become a "prompt engineer".
We are launching AgentSpot with over 50 templates -each one born from an internal need, refined by a subject matter expert, and ready to help you drive your business autonomously. Get three custom agents for free. Learn more today.
