Building narrowly scoped, reusable automation blocks ("callable workflows") for tasks like lead enrichment creates a composable architecture. When you need to swap a core vendor, you only update one central workflow instead of changing 50 different automations, ensuring business continuity and scalability.
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Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.
To build a durable business on top of foundation models, go beyond a simple API call. Gamma creates a moat by deeply owning an entire workflow (visual communication) and orchestrating over 20 different specialized AI models, each chosen for a specific sub-task in the user journey.
Block is re-architecting its entire business by treating all functions—from payments to HR—as a collection of capabilities. These are unified and accessed through a central AI agent middleware layer (Goose), orchestrating workflows across previously siloed product and corporate functions.
Instead of relying on one-off prompts, professionals can now rapidly build a collection of interconnected internal AI applications. This "personal software stack" can manage everything from investments and content creation to data analysis, creating a bespoke productivity system.
Vercel's CTO Malte Ubl notes that durable, resumable workflows are not a new invention for AI agents. Instead, they are a fundamental computer science concept that has been implemented ad-hoc in every transactional system, from banking in the 70s to modern tech giants, just without a standardized abstraction.
The primary value of AI app builders isn't just for MVPs, but for creating disposable, single-purpose internal tools. For example, automatically generating personalized client summary decks from intake forms, replacing the need for a full-time employee.
Using a composable, 'plug and play' architecture allows teams to build specialized AI agents faster and with less overhead than integrating a monolithic third-party tool. This approach enables the creation of lightweight, tailored solutions for niche use cases without the complexity of external API integrations, containing the entire workflow within one platform.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
When developing AI capabilities, focus on creating agents that each perform one task exceptionally well, like call analysis or objection identification. These specialized agents can then be connected in a platform like Microsoft's Copilot Studio to create powerful, automated workflows.
Marketers often buy specialized SaaS tools for tasks like lead routing. These are often just a database, workflows, and an AI model, which can be replicated for a fraction of the cost using an orchestration platform like Zapier. This approach provides more control and customization over your marketing stack.
