Grounding

Why it matters

LLMs hallucinate when they generate plausible-sounding answers without grounding in real data. The model knows what answers usually look like; it sometimes produces a confident answer that is wrong because the model is filling in patterns rather than checking facts.

Grounding is the defense. Instead of asking the model "what is my account balance," the agent calls the user's bank API and gives the model the real number. Instead of asking the model "what is on my calendar," the agent reads the calendar and provides the real events. The model's job becomes "format and contextualize the verified data" rather than "guess what the data probably is."

Common grounding techniques:

• Tool calls. The agent calls a real API and grounds the response in the API's output.
• Retrieval-augmented generation (RAG). The agent searches a document corpus and gives the model the relevant excerpts as context.
• Structured data injection. Calendar events, contact records, account state injected directly into the prompt.

For agent products, grounding is the difference between "useful most of the time" and "trustworthy enough to act on." A grounded agent that says "your balance is $4,217" is reading from the bank; a non-grounded agent saying the same thing might be fabricating. The user can rely on the first; not the second.

How Ajeris handles this

Ajeris grounds factual questions in real tool calls. Calendar questions go through the calendar tool. Account questions go through the relevant integration. Memory recall goes through the per-user memory store. The model's role is shaping the response; the data comes from real systems.

For questions where grounding is not possible (the user asks the agent's opinion, asks a general-knowledge question), the agent's response is clearly framed as "my read" rather than as fact.