3 Steps to Onboard AI Hires with Context Engineering
▼ Summary
– Successful AI agent adoption depends on “context engineering,” which involves providing the AI with comprehensive institutional knowledge like company culture, processes, and application data.
– Context must be specific to the agent’s role, complete, and AI-readable to avoid overwhelming its processing limits and to prevent errors like hallucinations.
– Organizations must assess and prepare three key content types for AI: company culture (often unstructured), documented business processes, and application configuration metadata.
– Preparing context requires answering critical questions about each content type’s existence, ownership, accuracy, structure, and security for AI accessibility.
– Implementing AI agents is driving a business process reengineering revolution, as they require more detailed, unambiguous, and up-to-date documentation than humans do.
Integrating AI agents into your business operations successfully hinges on a crucial practice known as context engineering. Just as a new human employee needs time to absorb company culture and processes, an AI requires a structured infusion of institutional knowledge to perform effectively. This process involves curating and preparing all relevant data, metadata, and process flows to ensure your AI tools can operate with precision and avoid the pitfalls of ambiguity that lead to unreliable outputs.
Consider why a newly hired expert doesn’t immediately outperform existing staff. The difference is institutional knowledge, the nuanced understanding of company culture, processes, applications, and team dynamics. For AI agents, this knowledge is called context. While you can deploy an AI in minutes instead of months, its performance is directly tied to the quality and breadth of context you provide. This goes beyond simple customer data to encompass everything that defines how your organization operates.
When we discuss AI models with large context windows, like Claude or ChatGPT, it’s easy to assume they can ingest all company information. However, the reality is more complex. A single Salesforce configuration with high complexity can consume a vast number of tokens. You must be selective, providing only the context relevant to the AI agent’s specific role, which is the essence of context engineering.
Much of a company’s vital information is unstructured, found in annual reports, process diagrams, or employee handbooks. Human employees excel at interpreting these materials, using judgment to fill gaps. AI agents, while improving, can struggle with conflicts, nuances, or omissions in unstructured data, which is a primary source of AI hallucinations. Therefore, the context provided must be both comprehensive and formatted for AI comprehension, yet scoped precisely to avoid overwhelming the system.
The key is to map the end-to-end process the AI will perform. This scoping determines which pieces of context to pull from various applications and data stores. Companies like Salesforce acquire tools like Data360 and MuleSoft precisely to manage context at scale. Providing correct context means parsing data sources with a clear understanding of the target process, combining documented workflows with application configuration metadata. This isn’t just about what metadata exists, but understanding the ‘why’ and ‘how’ behind its dependencies.
Process documentation varies widely in quality. Front-office processes are often poorly documented, while back-office procedures in regulated industries tend to be robust. To leverage AI agents, organizations must streamline and optimize these processes, sparking a new wave of business process reengineering. The level of detail required for AI surpasses what humans typically need. Similarly, understanding app configuration through metadata is possible but often muddled by technical debt, requiring sophisticated, agentic workflows to analyze effectively.
Before feeding content to AI, ask five critical questions for each content type: Does it exist and who owns it? Is it current and valuable? Is it written for AI consumption? Where should it be stored for secure AI access? How should it be structured and tagged for efficient token usage?
Company culture content, like onboarding materials and brand guidelines, is often unstructured. AI needs it all at once. Ownership is typically scattered across teams like Marketing and HR, so aligning incentives is crucial. This content must be made AI-readable, filling context gaps that humans intuitively understand. Security is paramount, as aggregating isolated data sets can create new, sensitive insights. Structuring this amorphous data for token efficiency is a significant challenge.
Business operations and process documentation is the critical framework for AI action. Most organizations have processes documented, but they are frequently incomplete or outdated. The focus should be on the specific processes related to the AI agent’s role, documented with extreme detail to compensate for AI’s poor handling of ambiguity. Interestingly, AI can now help generate initial process diagrams from notes or metadata, which humans can then refine. The most vital process to document is the process of process improvement itself, ensuring AI agents have literally correct and current instructions to follow.
Application configuration data, stored as metadata, is highly structured and ideal for AI. However, it requires more than a simple list; dependencies must be included. While metadata is accurate, the volume from any enterprise application is enormous and can easily exceed token limits. Therefore, it must be carefully structured and tagged, directly related back to the operational processes the AI is executing.
A common adage states that only 7% of communication is the words themselves; the rest is tone and body language. We instruct AI with words, the 7%, and then wonder about hallucinations. We must provide the other 93% as context. This includes the relationship between a customer and the company, data priorities, process stages, urgency, and outcome value. This context itself must be provided through additional words and data, meaning we need context for the context.
Context engineering is the structured approach to onboarding AI. The knowledge already exists as institutional memory; the task is to make it accurate and unambiguous for AI consumption. For organizations aiming to deploy sophisticated AI agents, a three-step action plan is essential: First, document the AI agent’s scope and desired outcomes. Second, identify and audit the quality of all critical contextual information. Third, format this context within platforms capable of curating it specifically for AI agents.
(Source: ZDNET)
