Introducing a New Paradigm: Agentic Bottom-up Execution

The New Landscape of AI Solutions

As AI adoption accelerates across industries, new categories of AI tooling have emerged—each solving different parts of the “intelligent work” problem. The first and most familiar category is AI chatbots: conversational interfaces that answer questions, generate content, or assist with simple tasks using natural language. They are powerful for ad-hoc queries but do not manage work, state, or business context on their own.

Another category is standalone agentic frameworks that focus on building autonomous agents that can retrieve data, call tools, or run multi-step workflows. These systems give technical teams flexibility but often lack native integrations with the day-to-day operational context where work actually happens.

Then there are single-purpose AI agents — tools designed to automate one narrowly defined function. Examples include agents for email summarization, lead scoring, CV evaluation, or data entry. These are helpful but disconnected, creating fragmented experiences rather than unified business processes.

Finally, a new class of agentic execution frameworks is emerging—platforms positioned to run AI-driven workflows across the entire enterprize. Some of those aim to automatically detect patterns in how employees complete work and then autonomously optimize those workflows. Others, by contrast, focus on executing predefined processes, embedding agents directly into CRM records and structured flows. Both examples are powerful—yet both operate in a “top-down” paradigm, where processes are either inferred by the tools platform or tightly prescribed by the CRM.

GlobalContext: Agentic Bottom-up Execution Platform

GlobalContext approaches the problem very differently. Instead of inferring or enforcing processes from the top, it enables business users to define the actual shape of work—tasks, projects, dependencies, messages, deadlines, people, and workflows—directly in the platform. This creates a bottom-up model of task, project, and process management, where the system is shaped organically by how teams already operate.

Where Microsoft Agent 365/WorkIQ attempts to detect business process patterns and Salesforce AgentForce executes predetermined CRM workflows, GlobalContext serves as a flexible execution layer that lets users articulate their own processes explicitly, in a natural language or through structured tools.

At the same time standalone AI agentic frameworks require all data and context to be extracted, combined and presented upfront, with the need to then capture and store the results of their Agents' work elsewhere.  

Contrary to that, GlobalContext does not assume what the context or workflow should be; it captures how people actually work, and make this information available to the agents - both to acquire the necessary context as well as providing the results of their work inside the same context, seamlessly enhancing and expanding it, and keeping it up to date. 

This bottom-up architecture has several key implications:

• User-defined context becomes the execution engine. Messages, tasks, events, projects, documents, and custom objects form a unified graph—allowing AI agents to act with precise, real business context.
• AI agents run on top of this context. Instead of generic agents, GlobalContext enables context-aware agents that can read, write, update, and execute user-defined workflows across the entire workspace.
• Non-technical users design processes themselves. Without needing admin-level CRM configuration or complex enterprise automation tools, teams can define rules, workflows, and agent behaviours.
• Work evolves iteratively. As teams refine how they collaborate, the agentic layer updates accordingly—allowing a living, adaptive process model rather than a static top-down one.

In this model, GlobalContext is both the operating system for work and the execution environment for AI agents, bridging the gap between raw autonomy and the realities of day-to-day business operations. Unlike MSFT and Salesforce—which optimise or enforce workflows—GlobalContext empowers teams to create them and then let AI execute them reliably.

Use Cases & Real-Life Examples

GlobalContext is built around bottom-up process determination and flexible agentic execution, it supports a wide range of practical scenarios.

Example 1: Omnichannel communications: Automated Triage & Routing Agent. Deploy a screening agent to monitor your public inboxes (e.g., Sales, Support), instantly classifying every email as a new lead, existing client, partner inquiry, or spam. Acting as an intelligent dispatcher, this agent will analyze the specific intent of each message and automatically route it to the specialized GC Agent or person best equipped to handle it.

Example 2: New Customer Acquisition: Sales Agent – B2B Fiber (telecommunications) This GC Agent manages the complex sales lifecycle by validating service availability, gathering missing technical requirements, and drafting complete proposals for human review. It handles both new leads and upgrades, and upon quote acceptance automatically triggers the connected order fulfillment workflows.

Example 3: Solution Selling: Requirements Analysis & Pre-Sales Agent
Accelerate complex solution selling by having an agent instantly analyze incoming RFIs, RFPs and RFQs to extract critical scope, deadlines, and potential red flags. It evaluates the opportunity against your company’s portfolio fit, delivering a detailed picture that allows your team to fast-track qualification and proposal strategy.

Example 4: Product Management: Idea to Execution Agent
Capture product ideas, roadmap features, and specifications in a visual mindmap, instantly converting nodes into estimated tasks within your development platform (e.g., Jira) via MCP. This Agent bridges strategy and execution, tracking every feature from initial cost assessment to backlog placement and active sprint implementation, giving you a unified view of progress.

Example 5: Cross-Department Collaboration: Agile Workflow Agent
Empower Operations, Customer Success, and Product teams to co-create a shared intake-to-resolution process without complex admin setup. Each team contributes their own steps and context to a unified mind map, while an intelligent agent orchestrates the routing and handoffs, allowing the workflow to evolve organically as teams refine their needs. 

Summary: A New Model for Agentic Work

GlobalContext takes a unique and different path.
It treats users and teams — not the enterprise architecture — as the source of truth for how work actually happens. Instead of detecting or enforcing patterns, GlobalContext empowers business users to define, connect, and automate their own workflows from the bottom up, starting small, enabling the fail-proof sand box approach.

This results in:

• A natural way to organize team knowledge
• More adaptable processes,
• Agentic automation that reflects real-world work,
• and a system that grows alongside the team rather than constraining it.

Where enterprise platforms optimize for rigidity and top-down control, GlobalContext is built for flexibility, evolution, and user-driven logic. It is a new kind of agentic execution framework—one that finally aligns with how modern teams actually operate.