Kenco’s AI agents cut audit costs, speed RFPs

DeepFabric achieved a rapid and risk-managed transition from pilot to production by deploying six AI agents across Kenco’s commercial, operations, transportation, and client services within just three months. This swift deployment was underpinned by a rigorous proof-of-concept phase that allowed the team to identify and resolve potential failures in a non-production environment, ensuring zero disruption to Kenco’s extensive 3PL network, which spans 141 distribution facilities and 43 million square feet of warehouse space. As COO David Caines emphasized, all six agents went live without affecting a single customer, highlighting the effectiveness of DeepFabric’s cautious yet accelerated approach.

The deployment yielded significant operational efficiencies, notably a 45% reduction in audit spend and up to a 30% acceleration in request-for-proposal response times, directly addressing Kenco’s critical pain points in managing labor-intensive manual audits prone to failure across multiple parties. These measurable gains not only demonstrate the tangible value of agentic AI in complex 3PL environments but also validate DeepFabric’s strategy of starting small and scaling fast with minimal risk.

Buoyed by these early successes, Kenco plans to expand its AI agent deployment to 20 supply chain agents across its North American 3PL operations within the next 12 months. This planned scale-up reflects strong confidence in DeepFabric’s solution to sustainably enhance operational efficiency and resilience across Kenco’s vast logistics footprint.

Unified governance and orchestration layers have emerged as indispensable foundations for managing the explosive growth and complexity of AI agents in enterprise environments. Platforms like MuleSoft Agentic Fabric and xpander.ai provide centralized control planes that unify agent discovery, intelligent routing, and observability across diverse models and infrastructures, preventing fragmented deployments and isolated data silos that stifle continuous data enrichment. Gartner’s projection that Fortune 500 companies will host over 150,000 AI agents by 2028 underscores the urgency, yet only 13% of organizations feel adequately prepared with proper governance frameworks, highlighting a critical gap in enterprise readiness.

Security, compliance, and accountability are not mere afterthoughts but the primary accelerants or bottlenecks in scaling agentic AI. The proliferation of consumer-grade AI tools that circumvent security controls has heightened supply chain risks, making robust governance layers with role-based access, identity management, and policy enforcement essential. Industry leaders like Pablo Pires and Lotte Vanden Wyngaert emphasize building shared platform tooling and runtime guardrails to enforce uncompromising security boundaries, as the prevalent 'slap it on' AI strategies only invite avoidable vulnerabilities and operational chaos.

Vendor-neutral control planes, exemplified by xpander.ai, represent a strategic evolution to avoid lock-in at the AI model or infrastructure level by orchestrating execution, permissions, observability, and lifecycle management across heterogeneous environments. However, this architectural shift introduces a new dependency on the control plane itself, as proprietary elements like xpander’s Universal Harness coordinate critical governance functions, raising concerns about portability and migration. This nuanced trade-off demands that enterprises carefully evaluate governance solutions not only for flexibility but also for long-term operational sovereignty.

As agentic AI moves decisively from pilot projects into broad production, enterprises are prioritizing governance frameworks that balance autonomous agent operation with human oversight. Surveys reveal that 83% of organizations place guardrails on equal footing with AI model intelligence, and nearly 60% already run agents autonomously within defined compliance boundaries. This shift reflects a maturing understanding that scalable, responsible AI deployment hinges on integrated control planes that manage agent identity, audit trails, and dynamic access policies, ensuring security without stifling innovation.

DeepFabric’s approach to AI integration at Kenco exemplifies embedding AI as a natural extension of existing workflows rather than as an add-on, allowing users to automate and enhance their current processes seamlessly. By enabling AI agents to act as co-pilots, specialists can broaden their expertise—such as a Windows technician resolving Mac issues—without altering their established workflows, thereby boosting efficiency and flexibility. Additionally, AI-driven intelligent routing and preemptive issue resolution empower users to address problems within familiar interfaces like Slack, minimizing disruption and preserving legacy systems as valuable operational assets.

Despite the advantages, integrating AI into legacy supply chain platforms remains challenging due to their outdated architectures, some over 30 years old and predating cloud computing, which limits their capacity to support AI-native applications and rapid cause-and-effect analytics essential for scaling agentic AI. To circumvent these constraints, modern strategies favor connecting AI models directly to enterprise data lakehouses rather than proprietary databases, ensuring data integrity and allowing AI to drive decision-making based on unbiased, comprehensive datasets rather than pre-existing hypotheses.

Rather than replacing legacy software, DeepFabric and similar innovators treat these systems as strategic assets, augmenting them by interacting with them as a human would—especially since many lack modern APIs—thus avoiding lengthy, traditional IT projects that attempt exhaustive data mapping and re-engineering. This pragmatic approach maintains existing user interfaces and workflows, enabling new AI capabilities to operate transparently within familiar environments, which preserves user habits and system integrity while accelerating AI deployment and value creation.

Successfully scaling agentic AI from individual pilots to enterprise-wide collaboration hinges on meticulous end-to-end workflow mapping and standardization to ensure AI agents align with real-world processes. Companies like PromptCare and General Assembly demonstrate that without clearly defined, repeatable workflows owned by accountable teams, automation efforts falter, as inconsistent processes undermine AI integration and diminish trust in outputs.

Multi-agent architectures that mirror organizational roles and responsibilities are essential for orchestrating complex, multi-step workflows at scale. General Assembly’s GAIA system, which reduced first-draft creation time by 90% while preserving human oversight, exemplifies how targeted automation of high-friction tasks combined with human-in-the-loop checkpoints ensures quality, compliance, and contextual judgment across enterprise functions.

Embedding forward-deployed engineers (FDEs) within customer environments is a critical strategy to bridge the gap between pilot success and production-grade AI deployment. As OpenAI and Anthropic invest heavily in FDE labor models pioneered by Palantir, these engineers translate AI capabilities into operational realities by integrating agents with legacy systems, building governance frameworks, and establishing continuous evaluation loops that sustain scalable, trusted AI workflows beyond initial prototypes.

Beyond technical integration, scaling agentic AI demands cultural readiness and workflow-first adoption approaches that transform isolated AI experiments into foundational business operations. Organizations like PromptCare address cultural resistance through transparent communication about AI’s role in shifting employee responsibilities toward exception handling, while rapid prototyping cultures at startups like Anthropic empower all team members to contribute iteratively, accelerating learning and embedding AI into the fabric of enterprise work.