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Inside Doco, an AI agent is described as a scoped operator: it establishes which knowledge base it can access, gathers evidence, makes a limited change when authorized, and checks the result. The workflow is browse → search → read → traverse → edit → watch—not download an entire knowledge base and rewrite it. This is Doco builder Harry Smart’s account of the designed workflow, not an independent product test.
What does an agent do after connecting to a knowledge base?
It works through six stages, each intended to reduce guesswork before a change and verify what happened afterward. The sequence is useful to understand even when the agent has read-only access: the first four stages are discovery and review, while editing requires write permission.
- Browse: Establish the connected workspace and knowledge base, whether it is local, staging, or production, what the access token permits, and the actual IDs and hierarchy of the intended target. This helps prevent acting on the wrong environment or a guessed identifier.
- Search: Find the relevant phrase or fact and assess whether the results cover the needed material. Doco’s article describes structured full-text search. An incomplete result does not prove the knowledge base has no answer; the agent should inspect more evidence or disclose that its coverage is incomplete.
- Read: Examine the document outline and nearby content, not just an isolated matching sentence. The surrounding blocks may contain constraints that change how the statement should be interpreted. The article describes continuation cursors for requesting further context without mixing document versions.
- Traverse: Follow relationships to related evidence. For example, before saying a rollback plan remains valid, the agent needs to inspect the linked rollback evidence. A relationship is a route to material to verify, not proof that the linked information is current or even intact.
- Edit: If authorized, reread the target block and its version, then make the smallest change justified by the evidence. Doco’s article describes attaching an
If-Matchprecondition so a write is guarded by the version the agent read. If the source has changed in the meantime, the agent must reread and reconsider rather than blindly retry. The HTTP standard definesIf-Matchas a precondition intended to prevent lost updates: RFC 9110, section 13.1.1. - Watch: Read the authoritative document back and confirm the intended value is present and nearby blocks remain intact. Then check whether search and indexes reflect the update, summaries are current, and related evidence still supports the result. If the change history is incomplete, Doco’s article says the agent should synchronize fully instead of claiming it has a complete change delta.
How the workflow handles a release-window change
Doco’s example request is to change a production release window from 20:00 to 20:30 and confirm that the rollback plan remains valid. The agent first identifies the correct release guide and target statement, then follows the relationship to the rollback evidence and inspects it. If the evidence supports the request and the agent has write access, it makes a targeted change and checks the document and related views afterward. The example illustrates the workflow; it is not a report of a change actually carried out.
Does an agent need write access to use Doco?
No. According to Doco’s article, a read-only agent can browse, search, inspect outlines, follow relationships, and watch for changes. It needs write access only to edit. Checking the token’s permissions before acting is part of establishing scope, not something to infer from the fact that a connection succeeded.
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How can an agent connect, and who controls its tools?
Doco’s article describes three connection routes: MCP, CLI, and REST API. It presents MCP as a way for compatible clients to discover tools, CLI as suited to terminal workflows, and REST as the integration foundation. It says the routes share documents, block IDs, versions, permissions, and errors.
The Model Context Protocol tools specification (2025-06-18) describes tools as model-controlled actions that models can discover and invoke, while leaving the interface pattern to individual implementations. Its security guidance says: “For trust & safety and security, there SHOULD always be a human in the loop with the ability to deny tool invocations.” That is protocol guidance; it does not mean every MCP client presents consent or approval in the same way.
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What can go wrong, and how should the agent respond?
- The write returns successfully, but the task may still be incomplete. A successful HTTP response does not itself establish that the intended document state is correct. Read the authoritative source back and inspect the result.
- A version conflict appears. The source changed after the agent read it. Reread the current version and decide how the requested change fits; a conflict does not merge competing intentions or determine which edit is right.
- Search returns nothing or too little. Treat this as a coverage limit, not proof that no answer exists. Inspect further or qualify the conclusion.
- A relationship, summary, or search result disagrees with the source. These are derived views and may be stale. Check the authoritative document and whether indexes or summaries have caught up.
- Change history is incomplete. Do not report a complete delta on partial history; synchronize fully first, as Doco’s article recommends.
What Doco’s article says about current limitations
At the time of publication, the first-party article says Doco uses structured full-text search rather than an all-knowing semantic search. It also describes a transient cursor for an API-connected agent’s pending block edit as future work. These are time-sensitive product statements from Harry Smart’s article, “What an AI Agent Actually Does Inside Doco,” published on DEV Community on September 29, 2026; they should not be read as independently verified or as a guarantee of the product’s current capabilities.
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