Part 3: The 10 Guidelines of Agentic AI DeploymentPage
Before you begin any project, you must commit to these 10 foundational principles. They are designed to mitigate risk, maximize return on investment, and ensure long-term strategic control.
Prioritize CSI-Owned and Controlled Platforms: Whenever possible, utilize CSI-owned and controlled platforms, such as raia, that meet stringent compliance and security standards. This approach minimizes vendor lock-in and, more critically, protects sensitive corporate data—including proprietary code, financial records, and customer information—that is used to train AI agents.
Maintain Control of AI Vector Stores and Training Data: Most AI use cases require the creation of a specialized knowledge base, which is stored in a vector database. It is imperative to maintain ownership and control over these vector stores and the underlying training data. This ensures that you can migrate your AI training data to a different platform or provider in the future without losing the valuable knowledge you have curated.
Retain Ownership of Agentic Workflows: Building effective Agentic AI solutions involves the development of complex workflows that connect to your internal applications and define the sequences of tasks for the AI to follow. You must ensure that you retain ownership and control of these workflows, allowing you to take them with you if you decide to switch platforms or vendors.
Use Models That Meet Enterprise-Grade Standards: Prioritize models that meet enterprise-grade security, auditability, and lifecycle requirements—regardless of vendor. While major providers like OpenAI, Google, and Anthropic are often the starting point, the key is to ensure the underlying model supports the isolation, governance, and upgrade paths your business requires. This future-proofs your guidance as the model landscape (including open-weight and on-premise models) evolves.
Deploy a Centralized AI Management Platform: Utilize a centralized platform, such as raia, to manage and control all your AI agents. This will provide a single pane of glass for monitoring all agent activity, managing conversations, controlling access, tracking token usage, and facilitating testing and training.
Adopt a Zero-Trust, API-First Architecture: An API-first approach, which treats AI agents as external applications, provides a strong foundation for security and governance. When implementing this model, focus on principles like scoped API access and data ownership.
Assign Clear Ownership: Every agent must have a named business owner responsible for outcomes, accuracy, and escalation behavior.
Use SaaS AI Add-Ons Tactically, Not Strategically: Use SaaS AI add-ons (e.g., Zendesk AI, GitHub Copilot) opportunistically for tactical gains, but never allow them to become the architectural foundation or the system of record for your intelligence, workflows, or training data. They can be excellent on-ramps for resource-constrained BUs, but should be evaluated as temporary leverage, not strategic assets.
Centralize Data Incrementally, Driven by Agent Needs: Centralize data where it creates immediate agent leverage, but avoid large, upfront data lake initiatives that delay value. Start with curated, agent-specific knowledge stores. Treat a unified data lake as an eventual convergence, not a prerequisite, to avoid getting bogged down in multi-year data engineering projects.
Balance Customization with Platform Acceleration: You should maintain the ability to build your own Agentic solutions, as customization is often required to drive the highest return on investment. At the same time, you should leverage platforms like raia to accelerate the deployment of certain internal use cases. This hybrid approach allows you to retain strategic control over your AI capabilities while also benefiting from the speed and efficiency of a managed platform.
Future-Proof Your Architecture Against Model Obsolescence: The underlying Large Language Models (LLMs) are evolving at an exponential rate. A custom solution built directly on one model (e.g., GPT-4) can become technically obsolete or economically uncompetitive when a superior model is released. A centralized platform (of which raia is one implementation) is designed to be model-agnostic, providing an abstraction layer that separates your agent's core logic from the specific LLM. This ensures upward mobility; when a new, better model becomes available, the platform handles the integration, allowing you to upgrade your agents with minimal rework and preventing vendor lock-in.
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