Volume is the wrong fix
Pasting whole documents and old tickets into the window buries the one fact that matters and spends the budget on text nobody checked.
Context your AI agents can check.
An agent works from what is in its context window. Muninark fills that window with facts from your own record, each traceable to a dated source, so the agent has something to cite when it would otherwise guess.
The problem
An agent fills gaps with plausible text.
A coding agent sees the files in front of it. It does not see why a rule exists or which decision constrains it. Where the context holds no fact, the model writes something that sounds right.
A fact needs a source
A summary with no reference gives the agent, and the reviewer after it, nothing to verify.
Old context goes wrong
A rule that held two releases ago still reads as true. Without dates and revisions, the agent cannot tell.
What goes into the context
Facts with a source, a date and a status.
Through MCP, an agent asks a question and gets a structured answer it can quote from.
- Source · The commit, ticket, decision or document each fact comes from, with a short verbatim excerpt where the text is indexed.
- Date · When the record was made, so an old fact reads as old.
- Status · Whether an expert approved a rule or decision, and whether newer information calls for reevaluation.
- Gaps · What the record does not say. With no supporting evidence, the answer says so and carries none.
- Access · Only what the person the agent works for is allowed to read.
How it reaches the agent
Tools the agent calls when it needs them.
The agent pulls evidence for the question in front of it, so the window holds what the current task needs. Which tools are enabled depends on the deployment.
Ask a question
Search and focused tools, such as decision trails, impact analysis and ticket context, return ranked evidence with citations.
Package the evidence
An evidence pack gathers the cited facts on one topic for a code review or a handoff.
Follow a procedure
A skill records the inputs, tool sequence, checks and stop conditions of a recurring task, so the agent follows the same steps each time.
Keeping the facts right
Facts that follow their sources.
What an agent receives is revised when its source changes and passes through the same review as everything else in the graph.
How knowledge stays current- Edits at the source become new revisions, and withdrawn text stops being served.
- Experts approve or reject extracted rules and decisions. A query can ask for approved records only.
- Ingested records are treated as data. The engine keeps its own instructions apart from record content and checks each model output against a schema.
Limits
What verified context does not fix.
- A model can still misread a correct fact. Review and tests stay in place.
- The graph holds what the record holds. Knowledge nobody wrote down stays missing, and the answer says so.
- We have not measured how far this reduces wrong answers. A pilot measures it on your own questions.
Where does your assistant guess?
Tell us about a question it gets wrong, and we can look at what your record could have told it.