What is an agentic knowledge base? Guru's Knowledge Agents discover, draft, verify, and retire company knowledge automatically. Human directed, agent led.
For as long as companies have kept knowledge, it had one audience. People. The job was human from end to end: capture what people know, keep it current, and help the next person find it when they need it. Now there’s a second audience, and it’s a lot less forgiving. AI agents are eager partners that know nothing about your company, your products, or your customers, and they’ll act on whatever knowledge they find, whether it’s right or not.
The agentic knowledge base is a set of Knowledge Agent capabilities that automate the full cycle of building and maintaining your company’s knowledge: discovering what’s missing, drafting it, organizing it, verifying it, and retiring what’s gone stale. Human directed and approved, agent led.
Want to see it live? We dove into this topic on a recent webinar. Watch the replay →
TL;DR
The big shift: Teams used to build and maintain knowledge by hand, with endless upkeep to keep things up to date and accurate. Knowledge agents run the work end to end, and pulls you in exactly when it needs you, not before.
Why it matters: Stale or wrong knowledge used to be a person’s problem. Now it’s an AI agent’s problem too, and agents repeat wrong answers with total confidence, at scale. Accuracy is the biggest blocker to AI transformation..
How it works: Knowledge Agents discover undocumented knowledge (from Slack, tickets, meetings, code), draft it, route it for approval, publish it, reorganize collections, verify continuously, and retire stale content — automatically.
It’s safe by design: You decide the agents autonomy, from fully human-managed to fully automated, permission-aware reads. Nothing publishes without approval unless you say so, and everything is versioned and reversible.
It gets more accurate over time: Every question asked, — by a person or another AI, is logged as answered, flagged, or unanswered, and the knowledge agent uses that record to fix its own gaps.
Your knowledge stays yours: Fully open via API, MCP, and CLI. Nothing is locked inside Guru, your knowledge is exclusively yours..
One system, two audiences: People and agents read, write and share the same governed, permissioned knowledge.
The proof: 2,000+ companies already running Guru in production.
Want to see it? Talk to a Guru expert, or watch the 2-minute demo.
Watch: the agentic knowledge base, live
Get a live look at Knowledge Agents discovering, drafting, and verifying knowledge in the replay of our agentic knowledge management webinar.
Why this, why now
When you hit a gap in what you know, you can shoulder-tap the person who actually has the answer. It’s inefficient, but possible. Your AI agents can’t. They read what’s in front of them and answer with total confidence, whether it’s right or not.
That changes the stakes. One employee misreading your refund policy is an annoyance. Twenty-seven agents repeating it with total confidence, to twenty-seven customers, is a different kind of problem. You usually find out about it after the fact, not before.
Search alone was never going to fix this. RAG, enterprise search, and MCP were each real steps forward, but each one ignored state. Semantic retrieval returns something plausible, not something correct, and the correct answer depends on the current, curated state of your knowledge. “Find yesterday’s meeting transcript” works. “How do we handle this for a UK customer on our business edition” falls apart, because the right answer changed last week and nobody updated the knowledge base.
And there’s a harder problem underneath staleness: a lot of what your company knows was never written down at all. The real answer lives in a Slack thread where a decision got made, a support ticket that keeps recurring, a sales call where someone explained how it actually works, a merged code change nobody documented. That’s the gap between what your company knows and what it’s captured and it’s the gap that starves every agent you build.
“Everyone wants to give your AI more context. We make sure the context is right.”
The shift: Knowledge agents run the work, you approve it
Knowledge Agents already work across all of your knowledge wherever it lives, answering, verifying, taking action. The agentic knowledge base is what lets them run the full build-and-maintain workflow end to end, deeply automated.
Picture the teammate who’s in every meeting, reads every ticket, and follows every thread. The one who occasionally taps you on the shoulder and says, “we never wrote that down, but we need to.” That’s your Knowledge Agent.
It reads the places knowledge actually lives — work chats, meeting recordings, support and project tickets, code changes — and surfaces what should be captured or updated. From there it drafts new knowledge, loops in the right human expert, publishes, reorganizes collections as products get renamed and pricing changes, verifies articles continuously, and unverifies knowledge that’s gone stale or unused.
The knowledge agent operates on its own, complete the work from start to finish, and pull in the right team members exactly when it needs them: to answer a question only they can answer, to review a draft, to approve a change before it publishes. You stay in control. You’re just brought in on demand instead of driving every step.
What that looks like in practice:
Discover: the agent watches Slack, Teams, meetings, tickets, and code for what should be captured or updated.
Draft: it writes the update, in your voice, from what it found.
Route: it loops in the person who can actually confirm it’s right.
Publish: the change goes live once your expert approves it.
Verify: articles get checked on an ongoing basis, not just when someone remembers to. New decisions in Slack or meetings automatically update existing knowledge.
Retire: knowledge that’s gone stale or stopped getting used gets unverified and hidden from search.
Before / After
Before: you write the knowledge, remember to find whats changing, remember to keep it accurate.
After: the agent runs the knowledge work, and pulls you in exactly when it needs a human judgment call.
You’re in control
Knowledge agents run on top of Guru’s full-featured knowledge base: versioning, rollback, and permissions at the collection, folder, and card level. That’s what makes agent-authored knowledge trustworthy instead of a liability, you can always see who changed what, and undo it if you need to. Agents can also publish approved knowledge out to systems like Confluence and SharePoint, so the workflow extends to wherever your knowledge already lives.
Knowledge agents are built on top of Guru’s extensive permission model. Your agent only reads what it has permission to see. It proposes; a human approves. Nothing publishes on its own unless you’ve explicitly set it up that way, and it narrates every step so you can see the trail, not just the result.
The loop that makes it more accurate over time
Knowledge agents improve their own accuracy. Every question asked by a person or another AI is captured in the AI Agent Center with its answer and status: answered, flagged inaccurate, or unanswered.
The agent examines that data set to close its own gaps and fix flagged knowledge. Accuracy climbs because the knowledge underneath got better. The more your people and agents use Guru, the more accurate Guru gets.
To be exact about what’s happening here: the agent is not training a model. It’s improving the knowledge it runs on, and that improvement is inspectable, versioned, and approved. No-answers spotlight knowledge gaps. Flagged answers point to stale knowledge. Both get acted on autonomously, with human approval.
Your knowledge is still yours
Everything Guru connects, and everything a knowledge agent generates, is your company’s knowledge, know-how, and expertise. It belongs to you, and it’s portable.
Context is not a moat. Any architecture that traps your knowledge inside an agent, a vendor’s product, or a context window is a liability, not a feature — it represents the collective know-how of your organization, and it should always belong to your organization exclusively.
In practice: your knowledge graph is yours, reachable by API, MCP, and CLI. The data is always yours. And your knowledge agents can draft and take action into your other systems, not only into Guru.
“No enterprise will accept a vendor locking their own company knowledge inside an agent.”
Built for your people and your agents
Knowledge now serves people and agents, and both read from and write to the same system. One permissioned, company-owned source of truth, connected to everything.
Splintered, per-tool context means every agent you build guesses differently. A shared, permission-aware layer is the only way your whole company stays consistent. Guru’s API, MCP Server, and CLI let any person or agent, from anywhere, read and write the knowledge they’re allowed to see, respecting the permissions you’ve already set at the organization level.
“There are two audiences for knowledge inside a company. There are people and there are agents. Both of them need it to do their work.”
Active, not passive
A doc repo or a context layer stores knowledge, governs it, and waits for your agents to read and write it. The reasoning and the work live in someone else’s agent, and the repo is just the memory.
The agentic knowledge base is active. Your Knowledge Agents run on their own to build and maintain the knowledge, and pull in your people only when the work needs them. It’s the difference between a filing cabinet and a colleague who tells you when the file is wrong — agents answer inside a governed system that captures every question and its status, and build a first-party record of what they got right, wrong, and couldn’t answer, then act on it. A tool that only retrieves never builds that dataset. Guru does, and it gets more accurate the more it’s used.
The proof
2,000+ companies already running Guru in production
100+ source connectors across work chats (Slack, Microsoft Teams), projects (Jira, ServiceNow, Asana, Linear, ClickUp, Notion), support tickets (Salesforce, Zendesk), and meetings (Zoom, Teams, Gong).
Permissions infrastructure across both the Guru knowledge base and every source it’s connected to.
A fully owned, portable graph: API, MCP, CLI: your knowledge is always yours.
Want the full walkthrough? Our team showed the agentic knowledge base working on real knowledge, from discovery to verification, at the launch webinar. Watch the replay →
Questions we expect you’ll ask
Will an agent publish changes to our knowledge base without anyone checking them first?
Only if you want it to. You choose where you sit on the dial — from every change reviewed by your team, to agent-drafts-you-approve, to fully autonomous. At every setting, the agent tells you what it did.
Is the agent reading our Slack messages and meeting recordings a form of surveillance?
No. It only reads what the person asking, or the document’s owner, could already see. it respects the same permissions you’ve already set. It proposes what it finds; it doesn’t act on its own without your say-so, and it narrates every step so you can see exactly what it looked at and why.
What tools does this connect to?
Over 100 sources across the places your knowledge actually lives — work chats like Slack and Microsoft Teams, project tools like Jira, ServiceNow, Asana, Linear, ClickUp, and Notion, support tools like Salesforce and Zendesk, and meetings in Zoom, Teams, and Gong. Your agent can also publish approved knowledge out to other knowledge stores such as Confluence and SharePoint.
Is our knowledge locked into Guru?
No. Your knowledge graph is fully available through API, MCP, and CLI, and the knowledge is always yours. Your agents can take action into other systems, not only into Guru.
What happens if the agent gets something wrong?
It’s built to catch that. Every question your agent answers gets logged and flagged if it’s wrong, and the agent reads that record back to fix its own gaps.Because everything runs on Guru’s versioning and rollback, you can always see what changed and undo it.
Does this replace other agents and chat tools we already use?
No. Guru is a verified knowledge layer that can be connected into any AI chat tool or agent you already have. The agentic knowledge base is about making sure what they find is actually correct, and keeping it that way as your business changes. We make Claude, GPT, and Copilot work better; we’re not competing with them.
See it on your own knowledge
Bring a real Slack channel, a support queue, or a stale collection, and we’ll show you exactly what your agent would find, draft, and flag. No slideware.
For as long as companies have kept knowledge, it had one audience. People. The job was human from end to end: capture what people know, keep it current, and help the next person find it when they need it. Now there’s a second audience, and it’s a lot less forgiving. AI agents are eager partners that know nothing about your company, your products, or your customers, and they’ll act on whatever knowledge they find, whether it’s right or not.
The agentic knowledge base is a set of Knowledge Agent capabilities that automate the full cycle of building and maintaining your company’s knowledge: discovering what’s missing, drafting it, organizing it, verifying it, and retiring what’s gone stale. Human directed and approved, agent led.
Want to see it live? We dove into this topic on a recent webinar. Watch the replay →
TL;DR
The big shift: Teams used to build and maintain knowledge by hand, with endless upkeep to keep things up to date and accurate. Knowledge agents run the work end to end, and pulls you in exactly when it needs you, not before.
Why it matters: Stale or wrong knowledge used to be a person’s problem. Now it’s an AI agent’s problem too, and agents repeat wrong answers with total confidence, at scale. Accuracy is the biggest blocker to AI transformation..
How it works: Knowledge Agents discover undocumented knowledge (from Slack, tickets, meetings, code), draft it, route it for approval, publish it, reorganize collections, verify continuously, and retire stale content — automatically.
It’s safe by design: You decide the agents autonomy, from fully human-managed to fully automated, permission-aware reads. Nothing publishes without approval unless you say so, and everything is versioned and reversible.
It gets more accurate over time: Every question asked, — by a person or another AI, is logged as answered, flagged, or unanswered, and the knowledge agent uses that record to fix its own gaps.
Your knowledge stays yours: Fully open via API, MCP, and CLI. Nothing is locked inside Guru, your knowledge is exclusively yours..
One system, two audiences: People and agents read, write and share the same governed, permissioned knowledge.
The proof: 2,000+ companies already running Guru in production.
Want to see it? Talk to a Guru expert, or watch the 2-minute demo.
Watch: the agentic knowledge base, live
Get a live look at Knowledge Agents discovering, drafting, and verifying knowledge in the replay of our agentic knowledge management webinar.
Why this, why now
When you hit a gap in what you know, you can shoulder-tap the person who actually has the answer. It’s inefficient, but possible. Your AI agents can’t. They read what’s in front of them and answer with total confidence, whether it’s right or not.
That changes the stakes. One employee misreading your refund policy is an annoyance. Twenty-seven agents repeating it with total confidence, to twenty-seven customers, is a different kind of problem. You usually find out about it after the fact, not before.
Search alone was never going to fix this. RAG, enterprise search, and MCP were each real steps forward, but each one ignored state. Semantic retrieval returns something plausible, not something correct, and the correct answer depends on the current, curated state of your knowledge. “Find yesterday’s meeting transcript” works. “How do we handle this for a UK customer on our business edition” falls apart, because the right answer changed last week and nobody updated the knowledge base.
And there’s a harder problem underneath staleness: a lot of what your company knows was never written down at all. The real answer lives in a Slack thread where a decision got made, a support ticket that keeps recurring, a sales call where someone explained how it actually works, a merged code change nobody documented. That’s the gap between what your company knows and what it’s captured and it’s the gap that starves every agent you build.
“Everyone wants to give your AI more context. We make sure the context is right.”
The shift: Knowledge agents run the work, you approve it
Knowledge Agents already work across all of your knowledge wherever it lives, answering, verifying, taking action. The agentic knowledge base is what lets them run the full build-and-maintain workflow end to end, deeply automated.
Picture the teammate who’s in every meeting, reads every ticket, and follows every thread. The one who occasionally taps you on the shoulder and says, “we never wrote that down, but we need to.” That’s your Knowledge Agent.
It reads the places knowledge actually lives — work chats, meeting recordings, support and project tickets, code changes — and surfaces what should be captured or updated. From there it drafts new knowledge, loops in the right human expert, publishes, reorganizes collections as products get renamed and pricing changes, verifies articles continuously, and unverifies knowledge that’s gone stale or unused.
The knowledge agent operates on its own, complete the work from start to finish, and pull in the right team members exactly when it needs them: to answer a question only they can answer, to review a draft, to approve a change before it publishes. You stay in control. You’re just brought in on demand instead of driving every step.
What that looks like in practice:
Discover: the agent watches Slack, Teams, meetings, tickets, and code for what should be captured or updated.
Draft: it writes the update, in your voice, from what it found.
Route: it loops in the person who can actually confirm it’s right.
Publish: the change goes live once your expert approves it.
Verify: articles get checked on an ongoing basis, not just when someone remembers to. New decisions in Slack or meetings automatically update existing knowledge.
Retire: knowledge that’s gone stale or stopped getting used gets unverified and hidden from search.
Before / After
Before: you write the knowledge, remember to find whats changing, remember to keep it accurate.
After: the agent runs the knowledge work, and pulls you in exactly when it needs a human judgment call.
You’re in control
Knowledge agents run on top of Guru’s full-featured knowledge base: versioning, rollback, and permissions at the collection, folder, and card level. That’s what makes agent-authored knowledge trustworthy instead of a liability, you can always see who changed what, and undo it if you need to. Agents can also publish approved knowledge out to systems like Confluence and SharePoint, so the workflow extends to wherever your knowledge already lives.
Knowledge agents are built on top of Guru’s extensive permission model. Your agent only reads what it has permission to see. It proposes; a human approves. Nothing publishes on its own unless you’ve explicitly set it up that way, and it narrates every step so you can see the trail, not just the result.
The loop that makes it more accurate over time
Knowledge agents improve their own accuracy. Every question asked by a person or another AI is captured in the AI Agent Center with its answer and status: answered, flagged inaccurate, or unanswered.
The agent examines that data set to close its own gaps and fix flagged knowledge. Accuracy climbs because the knowledge underneath got better. The more your people and agents use Guru, the more accurate Guru gets.
To be exact about what’s happening here: the agent is not training a model. It’s improving the knowledge it runs on, and that improvement is inspectable, versioned, and approved. No-answers spotlight knowledge gaps. Flagged answers point to stale knowledge. Both get acted on autonomously, with human approval.
Your knowledge is still yours
Everything Guru connects, and everything a knowledge agent generates, is your company’s knowledge, know-how, and expertise. It belongs to you, and it’s portable.
Context is not a moat. Any architecture that traps your knowledge inside an agent, a vendor’s product, or a context window is a liability, not a feature — it represents the collective know-how of your organization, and it should always belong to your organization exclusively.
In practice: your knowledge graph is yours, reachable by API, MCP, and CLI. The data is always yours. And your knowledge agents can draft and take action into your other systems, not only into Guru.
“No enterprise will accept a vendor locking their own company knowledge inside an agent.”
Built for your people and your agents
Knowledge now serves people and agents, and both read from and write to the same system. One permissioned, company-owned source of truth, connected to everything.
Splintered, per-tool context means every agent you build guesses differently. A shared, permission-aware layer is the only way your whole company stays consistent. Guru’s API, MCP Server, and CLI let any person or agent, from anywhere, read and write the knowledge they’re allowed to see, respecting the permissions you’ve already set at the organization level.
“There are two audiences for knowledge inside a company. There are people and there are agents. Both of them need it to do their work.”
Active, not passive
A doc repo or a context layer stores knowledge, governs it, and waits for your agents to read and write it. The reasoning and the work live in someone else’s agent, and the repo is just the memory.
The agentic knowledge base is active. Your Knowledge Agents run on their own to build and maintain the knowledge, and pull in your people only when the work needs them. It’s the difference between a filing cabinet and a colleague who tells you when the file is wrong — agents answer inside a governed system that captures every question and its status, and build a first-party record of what they got right, wrong, and couldn’t answer, then act on it. A tool that only retrieves never builds that dataset. Guru does, and it gets more accurate the more it’s used.
The proof
2,000+ companies already running Guru in production
100+ source connectors across work chats (Slack, Microsoft Teams), projects (Jira, ServiceNow, Asana, Linear, ClickUp, Notion), support tickets (Salesforce, Zendesk), and meetings (Zoom, Teams, Gong).
Permissions infrastructure across both the Guru knowledge base and every source it’s connected to.
A fully owned, portable graph: API, MCP, CLI: your knowledge is always yours.
Want the full walkthrough? Our team showed the agentic knowledge base working on real knowledge, from discovery to verification, at the launch webinar. Watch the replay →
Questions we expect you’ll ask
Will an agent publish changes to our knowledge base without anyone checking them first?
Only if you want it to. You choose where you sit on the dial — from every change reviewed by your team, to agent-drafts-you-approve, to fully autonomous. At every setting, the agent tells you what it did.
Is the agent reading our Slack messages and meeting recordings a form of surveillance?
No. It only reads what the person asking, or the document’s owner, could already see. it respects the same permissions you’ve already set. It proposes what it finds; it doesn’t act on its own without your say-so, and it narrates every step so you can see exactly what it looked at and why.
What tools does this connect to?
Over 100 sources across the places your knowledge actually lives — work chats like Slack and Microsoft Teams, project tools like Jira, ServiceNow, Asana, Linear, ClickUp, and Notion, support tools like Salesforce and Zendesk, and meetings in Zoom, Teams, and Gong. Your agent can also publish approved knowledge out to other knowledge stores such as Confluence and SharePoint.
Is our knowledge locked into Guru?
No. Your knowledge graph is fully available through API, MCP, and CLI, and the knowledge is always yours. Your agents can take action into other systems, not only into Guru.
What happens if the agent gets something wrong?
It’s built to catch that. Every question your agent answers gets logged and flagged if it’s wrong, and the agent reads that record back to fix its own gaps.Because everything runs on Guru’s versioning and rollback, you can always see what changed and undo it.
Does this replace other agents and chat tools we already use?
No. Guru is a verified knowledge layer that can be connected into any AI chat tool or agent you already have. The agentic knowledge base is about making sure what they find is actually correct, and keeping it that way as your business changes. We make Claude, GPT, and Copilot work better; we’re not competing with them.
See it on your own knowledge
Bring a real Slack channel, a support queue, or a stale collection, and we’ll show you exactly what your agent would find, draft, and flag. No slideware.
