AI Orchestration: Tacit Knowledge Extraction for Multi-Agent Workflows

Deep Dive

The enterprise is poised for a fundamental shift in 2026, moving from isolated AI tools to coordinated, multi-agent systems that will function as an "orchestration layer." This evolution will redefine how work is structured and executed across large organizations, driven by the need to extract and operationalize tacit knowledge, accelerate legacy system replacement, enable complex human-agent collaboration, and build commercially defensible, outcome-driven platforms.

The core implication of this shift is the transformation of enterprise workflows from sequential, human-driven processes to parallel, agent-coordinated operations. Seema Amble highlights that organizations, particularly the Fortune 500, possess vast amounts of siloed institutional knowledge and operational complexity often residing in human minds. Extracting this "tacit knowledge" through documentation and observation, and converting it into usable operational context, becomes the gating factor for effective multi-agent systems. Without this context layer, autonomous agents could operate in isolation, potentially leading to cascading failures or misaligned objectives. The ability to provide feedback across agents and measure holistic business outcomes, rather than isolated efficiency metrics, will be critical. This creates a parallel to human organizational failures, suggesting that robust evaluation functions and objective metrics will be necessary to guide agent behavior and prevent negative outcomes. For Fortune 500 companies, this presents an opportunity to accelerate the implementation of new systems and streamline operations previously hindered by bureaucratic complexities and fragmented data across disparate geographies and software systems.

Angela Strange emphasizes that this transformation is particularly acute in financial services and insurance, where the risk of not replacing outdated legacy systems now outweighs the risk of change. Modern, AI-native infrastructure unifies data from various sources, creating a new system of record. This unification enables parallelized workflows, allowing tasks like loan underwriting to be performed concurrently rather than sequentially. For instance, a mortgage team could see and execute hundreds of tasks in parallel, with agents handling routine aspects. This expansion of workflow categories also facilitates the integration of diverse data sources--from onboarding and KYC to transaction monitoring and customer service interactions--into unified risk and compliance platforms. The implication for builders is significant: these next-generation platforms offer 10x growth potential not only due to larger software categories but also by consuming labor-intensive tasks that were previously bottlenecks. Companies that adopt these "plumbing" upgrades will gain a substantial competitive advantage, potentially turning low-margin businesses into high-margin ones by improving speed, scale, and operational efficiency.

Alex Immerman details the practical manifestation of these coordinated systems within software, describing the evolution of vertical AI from information retrieval and reasoning to "multiplayer mode." This means multiple humans and agents will collaborate within a workflow, governed by explicit trust rules and a command center interface. This interface will differentiate between agent-executed actions and human-reviewed escalations, fostering trust and enabling agents to perform increasingly complex tasks, such as negotiating transactions within defined parameters. For vertical software companies, this evolution significantly increases their defensibility. Brand recognition, proprietary technology, and network effects from multiplayer collaboration create higher switching costs, making these platforms more resilient against horizontal AI solutions. The ability to embed agents deeply within specific industry workflows, manage trust, and provide a clear interface for human oversight moves work from being solely about "doing" to a more strategic emphasis on "reviewing" and directing.

Finally, David Haber provides the commercial filter, arguing that truly defensible AI companies will be those where AI reinforces the business model by driving revenue and measurable outcomes, rather than solely focusing on cost reduction. Companies that own the end-to-end workflow, from intake to outcome, and embed themselves deeply within customer operations, create unique data assets. This proprietary outcome data, which is not publicly available for model training, allows these platforms to become smarter over time, enabling them to provide better guidance on case valuation, resource allocation, and demand letter strategies. This creates a compounding competitive advantage. For example, plaintiff law firms operating on contingency fees are driven to take on more clients and achieve better outcomes, reinforcing their business model. Similarly, loan servicing agents that improve collection rates, not just reduce call center costs, demonstrate the power of AI reinforcing core business objectives.

In essence, the enterprise orchestration layer represents a paradigm shift where AI moves from a supplementary tool to the fundamental engine of workflow, driving efficiency, enabling new operational models, and creating profound competitive advantages for those who can effectively harness coordinated agent intelligence and proprietary outcomes.