Your AI agent sees the data. It still needs to understand what it means

Context for AI agents is the set of definitions that makes the agent correctly interpret the data it already accesses: campaign history, event taxonomy, and the business rules of the operation itself. Without that context, the agent does answer, but the answer carries the same risk as any number read out of place: it looks right and is not.

An article published here on the blog about how to connect AI agents to marketing and sales data showed the three ingredients of a real connection: centralized data, a standardized access protocol, and real context from the operation. This text goes deep into the third ingredient, because it is the one that most people skip when setting up the agent, and the one that shows up most when the answer comes out wrong even with the right data in front of it.

What raw data without context means

Raw data is what is already in your database: rows of leads, campaigns, events, deals. The AI agent that has access to that database, via MCP or any other protocol, can count how many rows exist and sum a column. What it does not know right away is what each row means within your operation.

A common example: two stages of the sales funnel called “proposal sent” and “in negotiation” may, in practice, be the same stage recorded under different names by different salespeople. Without that information, the agent counts the two as distinct stages, and the number of deals in progress comes out larger than it really is.

The three types of context for AI agents

Context for AI agents is not a single document: it is a combination of three layers, and each one solves a different type of error.

• Campaign history: what has already run, under what name, in which channel, and with what result. Without it, the agent cannot tell a new campaign from a reactivation of the same campaign with an updated name.
• Event taxonomy: what each recorded event means in the funnel, such as “form submitted” or “meeting scheduled”, and at which stage it falls. Without it, the agent may count the same lead twice or ignore an event that should count as a conversion.
• Business definitions: what your operation calls an MQL, what counts as a closed sale, which ticket goes into the CAC calculation. Without it, the agent applies a generic definition, and the number does not match what the team already uses in meetings.

Why the agent gets it wrong even with the right data in front of it

The most common error is not the agent making up a number. It is the agent applying a generic rule where it should apply your operation's rule. A practical example: asking “how many leads came from paid campaigns this month” to an agent with no UTM context returns a count that includes poorly identified traffic, because it does not know that your naming convention treats a specific parameter as a paid source.

The open specification of the Model Context Protocol (MCP), published by Anthropic, solves the standardized access part: how the agent requests the data and how the source responds. It does not solve the interpretation part, and does not set out to. That part remains the job of whoever organizes the operation, and this is where context comes in.

How to record context for AI agents without relying on memory

The most direct way to solve this is to document the operation in a place that any connected agent can read before answering. That is the role of the organization documents registered in Laiki: UTM convention, event names, sales funnel stages, and account-specific caveats are written once, and the agent consults that context together with the data, instead of guessing the structure with every new question.

It is the same logic behind the Laiki MCP connector: the agent does not start from scratch in every conversation, because the account's context is already recorded before the first question arrives. Closing the event taxonomy together with the same UTM naming standard that supports any reliable report also goes into that base, so that the agent and the human team read the operation in the same way.

Frequently asked questions about context for AI agents

Is context for AI agents the same as training the model?

No. Training a model changes its parameters; context is the information the agent receives along with the question, about your specific operation. Neither requires technical knowledge from the person asking the question.

Do I need to manually write all the context of my operation?

Not necessarily. When the data platform already generates this record from what exists in the account, such as event names, CRM fields, and campaign history, the manual work drops considerably. What changes is having that context centralized in a single place, instead of scattered in the head of whoever builds the report every month.

Is an agent with no context at all useless?

It is not useless, but it makes more mistakes. It still helps with general questions and simple calculations; the risk appears when the question depends on a specific definition from your operation, and that is precisely where context makes the difference.

What remains of this combination

Context for AI agents is what separates an answer that looks right from an answer that is right for your operation. Before demanding precision from an agent, it is worth checking whether it has, besides access to the data, the definition of how your operation names and organizes what it has already recorded. To see how this works in practice with Laiki's native MCP, just talk to the team.