Enterprise Knowledge Agent
Answers questions from your own content with citations and permission awareness.
The problem. Every AI programme eventually stalls on the same thing: answers that cannot be traced to an approved source, and retrieval that either leaks content or hides it.
The agent is the retrieval layer other agents depend on: permission-aware search across your repositories with citation on every answer.
It respects source-system entitlements at query time, so two people asking the same question correctly get different answers.
Content freshness, ownership and coverage gaps are reported, so knowledge debt becomes visible and fixable.
Answers come from whichever document someone found, with no citation, no ownership and no way to audit.
Every answer is grounded, cited, entitlement-correct and measurable — and reusable by every other agent.
Where this agent sits in the stack.
Retrieval that respects permissions, cites its sources and is measured by an evaluation harness — not a demo index.
- 01
Answer surfaces
Search, copilots, agent assist and in-app answers, each returning citations the user can open and verify.
- 02
Retrieval & ranking
Hybrid keyword and vector retrieval, re-ranking, query rewriting and freshness rules tuned against a labelled evaluation set.
- 03
Index & embeddings
Chunking strategy, embedding models, metadata and access-control lists carried into the index so retrieval is permission-aware.
- 04
Content pipeline
Connectors and incremental sync from SharePoint, Confluence, ServiceNow, CMS, ticket history and document stores, with deletion propagation.
- 05
Source systems
The document, knowledge and record systems that own the content — the index never becomes the source of truth.
Integration surface
- SharePoint, Confluence, Drive and file repositories
- Intranet and policy sites
- Ticket and case history
- Identity provider for entitlement resolution
Guardrails & human oversight
- Permission-aware retrieval enforced at query time against source-system entitlements.
- No answer without a citation; unknowns are stated as unknowns.
- Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached.
- Content owners are notified when their documents drive low-quality answers.
What has to be true first
- An inventory of repositories with owners and access models.
- Agreement on which content is authoritative where duplicates exist.
- Identity data that maps people to entitlements.
Security, data & compliance
- Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account.
- Customer and employee data stays inside your tenancy and region; no training on your data by default.
- PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy.
- Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How this agent reaches production.
Weeks 1–2 · Scope
Repository inventory, entitlement model, authoritative-source decisions.
Weeks 3–6 · Build
Ingestion, chunking, hybrid retrieval, entitlement filtering, citation.
Weeks 7–10 · Production pilot
One domain live with a graded evaluation set and content-gap reporting.
Quarter 2+ · Scale & run
Enterprise coverage as a shared retrieval service for other agents.
What we agree to be measured on.
Ranges drawn from comparable production engagements. Your baseline is agreed before build starts, and the same numbers are reported after go-live.
| Metric | Expected range |
|---|---|
| Answers with a valid citation | 99%+ |
| Answer accuracy on the graded evaluation set | 90–96% |
| Time to find an authoritative answer | Minutes to seconds |
| Entitlement violations in testing | Zero tolerated |
Model the business case: AI Build vs Buy TCO calculator →
One knowledge domain live with a graded evaluation set, entitlement testing and content-gap reporting.
Fixed-price scope · milestone billing · price on request.
Enterprise retrieval service consumed by every agent, run against accuracy and freshness SLAs.
Retained pod · quarterly outcome review · price on request.
The full Enterprise Knowledge Agent specification, as a PDF.
A multi-page specification your architecture, security and procurement reviewers can read without a call: what the agent does, the architecture, the integration surface, autonomy and guardrails, security posture, rollout plan, measurement plan and engagement shape.
- Process before and after, with the decision that stays with a human
- Layered architecture diagram and named integration surface
- Guardrails, approval gates, escalation and audit trail
- Security, data handling and compliance posture
- Phase-by-phase rollout and the measurement plan
Building your first production-grade agentic workflow
A field-tested blueprint for shipping your first agentic AI workflow into production — the same one we use with Fortune 500 clients. Covers intent boundaries, tool design, memory, guardrails, human-in-the-loop patterns and evaluation harnesses so your first agent survives real users, real data and real audits.
Read the playbook →- Knowledge foundation for wealth advisory agents
Cited answers in under 10 seconds · 90%+ of responses carry an approved source
- Employee knowledge agent with permission-aware retrieval
2–4 search hours recovered per employee per month · 90%+ of answers returned with a citation
- Clinical knowledge foundation for patient-facing agents
20–35% higher answer accuracy vs. keyword search · one governed source behind every agent
What enterprise buyers ask about this agent.
Short, quotable answers on scope, systems, autonomy, governance, timeline, outcomes and commercial shape.
What does the Enterprise Knowledge Agent actually do?
Answers questions from your own content with citations and permission awareness. The agent is the retrieval layer other agents depend on: permission-aware search across your repositories with citation on every answer. It respects source-system entitlements at query time, so two people asking the same question correctly get different answers. Content freshness, ownership and coverage gaps are reported, so knowledge debt becomes visible and fixable.
Which systems does the Enterprise Knowledge Agent act in?
It works across Document repositories, Intranet / wiki, Ticketing history, Data catalogue. The named integration surface is SharePoint, Confluence, Drive and file repositories; Intranet and policy sites; Ticket and case history; Identity provider for entitlement resolution.
Which platforms is the Enterprise Knowledge Agent built on?
We deliver it on the platforms you already own — Azure OpenAI, AWS Bedrock, Copilot Studio. Pronix.ai is a systems integrator, not a software vendor, so the code, prompts and evaluation sets belong to you.
How much autonomy does it have?
Assistive autonomy. Permission-aware retrieval enforced at query time against source-system entitlements. No answer without a citation; unknowns are stated as unknowns. Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached. Content owners are notified when their documents drive low-quality answers.
What has to be true before we start?
An inventory of repositories with owners and access models. Agreement on which content is authoritative where duplicates exist. Identity data that maps people to entitlements.
How is it secured and governed?
Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account. Customer and employee data stays inside your tenancy and region; no training on your data by default. PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy. Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
How long does it take to reach production?
Delivery runs in 4 phases. Weeks 1–2 · Scope: Repository inventory, entitlement model, authoritative-source decisions. Quarter 2+ · Scale & run: Enterprise coverage as a shared retrieval service for other agents.
What results should we expect?
90–96% graded answer accuracy with citations on every answer. Expected ranges from comparable production engagements: Answers with a valid citation 99%+; Answer accuracy on the graded evaluation set 90–96%; Time to find an authoritative answer Minutes to seconds; Entitlement violations in testing Zero tolerated. Your baseline is agreed before build starts and reported on the same measures after go-live.
What does an engagement look like?
Production pilot: One knowledge domain live with a graded evaluation set, entitlement testing and content-gap reporting. Scale and run: Enterprise retrieval service consumed by every agent, run against accuracy and freshness SLAs. Fixed-price pilot with milestone billing, then a retained pod with quarterly outcome reviews.
What does the Enterprise Knowledge Agent cost?
Price on request. Every agent is scoped to your integration surface, data readiness and compliance posture, so we send a written estimate after a 30-minute scoping call rather than publish a band that would not apply to you.
Scope the Enterprise Knowledge Agent against your systems.
A 30-minute working session with the architects who deliver it: your integration surface, the decision that stays with a human, the measurement plan and a written estimate. We reply within one business day.
Get a written estimate for the Enterprise Knowledge Agent.
Tell us the process, the systems it touches and the compliance scope. We come back with a scope, a measurement plan and a written estimate — no published band that would not apply to you.
solutionEnterprise Knowledge Agent — routed to this team
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