What happens if your AI agent promises a customer a benefit that never existed?
Imagine, for example, a company chatbot that tells a customer it can resolve an issue with a customer’s delayed invoice payment. But what happens when it fails halfway through the attempt because it lacks full visibility into how invoices are actually approved internally? Unfortunately, without proper context engineering, this can happen easily and all too often in production. At its root, this problem is almost always to do with AI having general context but not specific context. This means that your agent knows generic things, but doesn’t have the years of accumulated knowledge that someone in Accounts Payable has accumulated over two decades navigating every possible blocker.
Identifying such policies is table stakes for a successful agent deployment. They lie scattered across your business. Everyone knows them intuitively, even if they’re not written down or are slightly out of date.
Everyone, that is, except your agent, because AI agents are not very good at intuition without context. Unfortunately, this never resides in a single drive or database marked “business context. Context isn’t all in one place, it’s spread out.
Where is it? Most of us assume that context is out there, somewhere. Maybe in an as-yet-undiscovered data silo. In many cases, though, the data AI agents need lies in uncharted territory.
Check out this video from our CEO, Justin Borgman explaining this exact topic.
In this article, we’ll look at why so much valuable business context remains unmapped, why writing it down is a necessary precursor to the efficiencies inherent to AI, and how to make that context accessible to your agents.
The blank spaces on the map
A lot of problems arise from the fact that agents can’t access your data, and Starburst has a particular strength on that front to its focus on data federation. But what if the problem is different? What if the problem isn’t that this data isn’t accessible, but that it doesn’t exist as data at all? That’s a different kind of problem. You can’t access non-data at all because it needs to become data first to be on the map in the first place. In this sense, telling AI to find it and work with it is like telling a ship captain to sail into the blank spaces on a map and take actions on the basis of it. Once they get to the map’s edge, they’re flying blind.
The data backs this up. Most organizations don’t think their data is AI-ready, and a big part of that involves the deficiencies around context engineering in an enterprise setting. A Harvard Business Review Analytic Services study found that only 7% of enterprises think their data is completely ready to serve the next generation of autonomous agents.
Across the survey, the majority (56%) said their main obstacle was difficulty accessing data and integrating data sources. But a far harder problem lingers beneath the surface.
The goal with AI agents is for them to have enough information to make autonomous decisions. To do that, they need to know what your employees know. In the invoice case above, for example, they need to know who’s involved in the approval process, what commonly causes processing delays, and common workarounds for known issues.
Accessing that information requires more than just setting up a new data connector. It requires writing it down in the first place so it doesn’t exist only in the heads of your employees.
The missing piece of the context puzzle
We can think of business context as falling into three categories:
- Technical context. This is the data about your data, or metadata, including descriptions, last updated times, owners, and data lineage.
- Business context. The metrics and key definitions, such as how revenue is calculated, that constitute the bleeding heart of your business. This is the language of how your business works.
- Process context. How various business workflows operate from end to end, along with the pressures, business culture, and capabilities that influence human decision-making, for example your company’s mission statement, or institutional knowledge about your competitors.
The first is easy to acquire and populate using existing technology. The second is possible, but requires more work, as it involves documenting the company’s most critical business definitions in a semantic layer.
Naming the territory
These blank spots on the map have existed for years. Sometimes, humans draw them in. Most of the time, they leave them blank. Or they write them down once and fail to update them when they change.
There have been good reasons why much of this stays uncharted. Familiarity means that traditionally, there has never really been a need, and organizations and people alike don’t tend to invest time and energy in things that aren’t needed. If you live in a neighborhood, you don’t need to draw a map of it. You know where the local grocery store and the post office are without writing this information down. You know to avoid turning down Harrold Street because it’s a dead end.
Human minds are context engines. That enables us to do a lot without committing it to paper. We can hold knowledge in our heads and pass it on to other humans, such as during the employee onboarding process. Writing this information down and maintaining it is extra work that falls by the wayside when things get busy. Additionally, before the advent of AI, there was no good way for machines to read and use this information, which limited its use in business automation.
In other words, sailing into uncharted waters has been hard work with little reward.
There’s a tendency for companies to say “let’s use AI to fix this.” But AI can’t help here by definition, at least not out of the box.
LLMs are like mixing machines. They can slice, dice, and recombine existing content. They need something original to work from. Without this raw material, they can’t author business context that no one’s ever recorded. If you try to force them, the answers will be hallucinatory and, often, hilariously wrong.
Mapping this uncharted territory requires humans to set sail and navigate it themselves. In data terms, it requires a human-led effort in context engineering to chart, describe, and label the no-man’s lands of your business context.
This mapping work requires changing how you think about documenting work, and developing a culture of turning context into data. The form of this will differ depending on what you need to capture. It may be a table connected to business logic held in a semantic layer, a description of a process, an Excel spreadsheet, or even a transcript from a recorded meeting.
What’s important is that this context is captured somehow and somewhere in a form that can be supplied to AI agents to enhance their decision-making.
What needs to be written down
Context engineering isn’t a one-off, it’s an ongoing process. You can focus on context periodically to try to catch up on what you’ve failed to document so far. But ultimately, you need tools and processes in place to write down the new context your business generates every week.
This context includes, but is not limited to:
Workflows detailing how things get done
This is perhaps the most critical gap businesses need to fill. If agents are going to act autonomously, they need to understand how your business operates.
This isn’t just limited to publicly available chatbots. AI agents need to understand the conditions, guardrails, and human approvals needed before making many business-critical decisions.
Without this context, agents might:
- Throw out a sales lead because of a low-quality score. The agent didn’t know that inquiries from named accounts should always go to their account executive regardless of lead score.
- Resolve a support ticket unilaterally with a link to a knowledge base article. The customer was a tier-1 account, and their issue was security-related, necessitating their Customer Success Manager be looped in first.
- Delete records and files it thinks aren’t in use. The company was required to keep those records due to a legal hold or to conform to data regulations such as GDPR.
Meaning detailing definitions and the rules behind them
It’s not enough to document how you calculate terms like active customer or churn. You need to provide the reasoning, logic, and rules behind that decision as well.
Many of these calculations reflect decisions that never make it into a document. Why do you count renewals the way you do? How do you identify (and exclude) test accounts? How do you score and distinguish real leads from junk queries?
A number without context is just a number. The rules and the reasoning behind them provide invaluable insights that agents can leverage to make autonomous decisions.
Connection detailing how data relates
Another common gap in knowledge is how data relates across data sources. For example, finance may have an “account” entity. Is that the same as the “customer” entity in the sales CRM? And what about the “org” entity in the product database?
These may all refer to the same entity, or they may refer to slight variations of one another. Often, this is the kind of knowledge a data engineer or senior analyst keeps in their head when devising new queries. They know intuitively how to perform a distributed join on a cleaned ID, not the raw one. In other words, they exercise judgement on the basis of their context.
This is the kind of thing that, without explicit knowledge, an agent attempts to infer. Sometimes, it’s right. Often, it isn’t.
Bringing data back from the badlands
It’s trendy to call agents coworkers. But most agents aren’t close to achieving that status, and they won’t be without overcoming the context gap.
That’s because we assume all the data they need is out there somewhere. But much of it isn’t anywhere accessible to agents. Most of it is still in employees’ heads, constituting a vast sea of uncharted territory.
Unless you can turn this context into data, your AI agent projects won’t succeed. They’ll always be operating on a partial, limited view of your business, and that is not sufficient. For agents to be true coworkers, you need to fully map the unexplored territory that your employees navigate almost on instinct.
Tools and technology can help here. Starburst ensures you can access data wherever it lives, and helps you build an enterprise context layer to give agents the context they need.
No tool in the world, though, can help your AI agents access territory that doesn’t exist on any map. Only by naming and describing these uncharted lands can you ensure that your AI agents have the context they need to make decisions that reflect how your business actually works.
Want to know more about how Starburst can access context wherever it lives? Read our whitepaper outlining how to build an agentic workforce with context in mind.
