As companies increasingly integrate artificial intelligence into their operations, many are learning a foundational truth: while a machine’s skills may be impressive, its lack of contextual understanding can lead to significant setbacks. Organizations that are onboarding their first AI agents are realizing that even the most advanced systems struggle without the institutional knowledge that human employees acquire over time. This has given rise to a new practice known as context engineering, which emphasizes the importance of providing AI agents with the cultural, procedural, and application-specific knowledge they require from day one.
Vendors have begun to tout models with expansive context windows, with some claiming to accommodate token limits in the millions. However, in practical applications, this vast capacity can still fall short against the complexities of enterprise knowledge. For instance, the configuration of a moderately complex cloud application or a few process maps can easily exceed these theoretical limits. Hence, what matters more than simply overwhelming an AI agent with information is the careful selection and timing of the context it receives.
The objective of this new approach is not to “teach” AI systems everything there is to know about a business; rather, it is to present them with the verified, role-specific context they need to reason accurately, minimize errors, and operate within defined parameters. This shift is reshaping how organizations approach the onboarding of AI technologies.
Why Context Beats Training For AI New Hires
Human employees can navigate ambiguities and fill in gaps using their judgment, drawing on unstructured artifacts like brand guidelines and informal discussions. While AI is making strides in interpreting unstructured text, it often falters when faced with conflicting sources or implied rules. The root cause of these failures is not weak models but rather inadequate context.
Research from institutions such as Stanford’s Human-Centered AI Institute has demonstrated that retrieval-based grounding can significantly enhance the factual accuracy and consistency of AI outputs when compared to naive prompting. Organizations like NIST have emphasized that the data surrounding AI—its lineage, provenance, and scope—are as crucial to trustworthy AI as the models themselves. Essentially, understanding the context of data, including its significance and appropriate application, is vital for reliable AI performance.
The complexities of enterprise architecture further illustrate this challenge. For example, a typical customer relationship management (CRM) stack consists of structured customer records, metadata for entitlement rules, outdated process diagrams, and unstructured brand tone guidance. Tools such as Salesforce Data Cloud, MuleSoft, and Tableau exist to manage this diverse array of information, and AI agents require a similar level of integration tailored to their specific tasks.
A Three-Step Context Engineering Plan For AI
The first step involves defining the role and mapping out the process. It’s essential to establish a clear end-to-end objective for the AI agent, such as “resolving priority-2 support cases within entitlement.” This requires drafting a straightforward process map detailing inputs, handoffs, service level agreements, and escalation rules. The next phase is to identify the minimal viable context necessary for achieving that outcome, focusing on relevant objects, fields, macros, and policies while avoiding the temptation to overload the agent with extraneous information.
For example, a service agent working within a CRM doesn’t need access to the entire codebase; instead, it should be equipped with the entitlement matrix, macro library, top-resolution notes, and compliance guidance relevant to case handling. In many organizations, just a handful of complex code classes can exceed hundreds of thousands of tokens, making dependency analysis essential for pulling only the information pertinent to the agent’s tasks.
The second step is to construct context pipelines and guardrails. Context should be treated as a dynamic dataset, integrating information from various sources such as Confluence, ticketing systems, and HR systems. This data should be normalized, tagged for authority and recency, and stored in a retrieval layer that supports both semantic and keyword searches. Additionally, establishing rules for when to use certain contexts and how to resolve conflicts between sources is critical for maintaining the integrity of the information provided to the AI agent.
The final step involves ongoing testing, measurement, and iteration. Organizations should create evaluations that simulate real task scenarios, such as resolving entitlement conflicts or drafting compliant communications under complex conditions. Metrics such as outcome quality, resolution time, and response accuracy grounded in retrieved sources should guide improvements. By maintaining versioned “context packs” that correlate with updates in business processes, companies can ensure their AI onboarding remains relevant.
Teams that adopt context engineering for AI onboarding report faster time to value, reduced instances of erroneous outputs, and streamlined governance. Industry leaders stress that value generation hinges more on effective data management than on cutting-edge models. However, risks remain, including the dangers of introducing outdated documents or excessive information that can overwhelm the AI’s processing capacity. By effectively managing these challenges, organizations can ensure their AI systems operate effectively, akin to well-prepared human colleagues.
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