Knowledge-Grounded AI Agents

A grounded agent must keep source authority, caller identity, citation support, and permission to act as separate decisions throughout its loop.

An internal document can support an answer without authorizing the next action. We build the agent around named source owners, identity-aware retrieval, visible citations, freshness and abstention rules, then keep tool authority in a separate control path.

One agent path stays inspectable in four parts, so answers, source access, abstention, and action authority can each be accepted or refused on their own.

Set the source rulesExplore AI agent development
Illustration of Knowledge-Grounded AI Agents: a team testing an AI agent's tools and decision boundaries

Some of the 500+ brands we've worked with

See all references
  • Mustela
  • Madame Coco
  • Shiftdelete
  • eOfis
  • TransferGo
  • Wall Street English

Steps, gates, and who decides

How we work

We settle source authority before building retrieval. Citation, freshness, abstention, instruction handling, and action permission each get their own test.

  1. Register the source and its authority

    With each source owner, we record the audience, access rule, sensitivity, freshness expectation, and the claims or decisions that source may support.

    AI assist
    Using the material you provide, the model drafts source entries for owner review.
    Human gate
    Does every in-scope source have an owner, audience, access rule, and permitted use? The source owner decides each entry's permitted use and freshness rule.

  2. Build retrieval around identity

    We connect ingestion, retrieval, context assembly, and citations to the caller's identity. Uncertainty, abstention, and stale-source behavior are handled in the same working slice.

    AI assist
    During the build, the model flags identity, access, and citation mismatches.
    Human gate
    Can each identity retrieve only the knowledge its job permits and show the supporting source? Your platform owner accepts the retrieval and access rules for each identity.

  3. Test support before action

    The test set covers unsupported claims, missing citations, conflicting or stale material, unauthorized retrieval, instruction-bearing documents, and attempts to turn evidence into an unapproved action.

    AI assist
    Conflicting, stale, and instruction-bearing cases come from the model, then a person reviews them.
    Human gate
    Can the agent answer, abstain, or escalate without inheriting permission to act? Critical grounding and authority failures go to your agent owner for a decision.

  4. Assign freshness and exception work

    Before handoff, we assign review, update, retirement, monitoring, exception, and escalation duties to named people, with a path for disputed or unavailable material.

    AI assist
    Test results give the model the inputs for a draft freshness and escalation runbook.
    Human gate
    Who responds when a source is stale, unavailable, disputed, or unsafe? The source owner accepts the cadence, exceptions, and response path.

Named artifacts you keep

What you get

You receive four working records. Source authority, retrieval design, test evidence, and ongoing freshness ownership each remain independently reviewable.

  • Policy

    Source authority and permitted-use register

    The approved source set with owners, audiences, access rules, authority status, sensitivity, freshness expectations, and permitted uses.

  • Architecture document

    Identity, retrieval, and citation design file

    The working design for ingestion, retrieval, context assembly, identity, citations, uncertainty, abstention, and a separate action-permission path.

  • Test evidence

    Grounding and action-authority findings report

    Typical and adverse cases covering source support, unauthorized retrieval, stale material, retrieved instructions, citations, abstention, and action authority.

  • Playbook

    Freshness ownership and source-exception notes

    The assigned source update, review, retirement, monitoring, exception, and escalation duties after handoff.

Scope and honest limits

When to bring us in

This build fits an agent that works from changing internal knowledge, especially when sources differ in authority, audience, access, or freshness.

A good fit when

  • Teams disagree about which internal document is current, so the agent can cite a source without anyone knowing whether it carries authority.
  • Retrieval already returns useful material, but identity rules and stale-source handling change between paths, making the same question behave differently.
  • A source owner can define authority, yet the agent owner still lacks a grounded test trace for accepting the behavior that follows.
  • Source entries list audience and access today, but permitted use and freshness expectations are not tied to the owner who must settle them.
  • Caller identity controls retrieval, yet citations, abstention, and stale-source behavior still vary as the agent assembles context.
  • Retrieved documents can carry instructions into the agent loop, but no tested control keeps that text from being treated as a command.
  • The agent has answer tests, while source support and permission to act are still scored together and can hide an authority failure.

Better handled as other work when

  • You want the document estate treated as equally trusted. Each identity instead needs an approved source and access rule.
  • Supporting evidence should grant tool authority on its own. The agent's action permission must remain in a separate owner-approved control path.
  • You need Zeo to own source updates or run the production agent after handoff. Those ongoing duties require a separate operating scope.

If one of these is closer to your situation, start here instead: Explore AI agent development

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

Content we've produced on this topic

Articleby Burak PehlivanWhat is WebMCP? Agentic Web and Websites Interacting with AI AgentsArticleby Yiğit KonurMCP Server Architecture: State Management, Security & Tool Orchestration

Tools we use

Tools behind this work

  • Cohere

    the reranker separating a merely similar document from one that actually supports the answer

  • LlamaIndex

    the retrieval framework connecting a chunk back to its source and its identity scope

  • Weaviate

    the vector store enforcing an identity's retrieval scope at the query level

  • Voyage AI

    the embedding model determining whether the right source is even found

  • Ragas

    the groundedness score turning 'does this citation support the claim' into a metric

  • Langfuse

    the trace recording which document version an agent actually used for a given answer

  • Lakera Guard

    the retrieval-content check catching an instruction embedded in a retrieved document

Next step

Ground the first agent job in owned sources

Tell us which job the agent should do, which sources may back it, and who settles access, freshness, and action authority. We'll build the smallest grounded path that makes each boundary inspectable.
Define the first source set

Before you decide

Questions we hear a lot

Use the smallest set that can support the real job. Each source needs an owner, audience, access rule, freshness expectation, and permitted use. Availability alone does not make an internal repository authoritative.