Abstract
Agents working over professional document collections face a structural failure: load entire files into context (prohibitively expensive) or search by filename alone (unreliably wrong). The Hippo Campus Protocol (HCP) separates memory from reasoning. Files are prepared in a local searchable library ahead of time; when a question is asked, agents receive compact triage cards — summaries, anchors, confidence signals, location references — before committing to full reads.
The agent’s document problem
When a user asks “find the plumber invoice from March” or “what does the contract say about termination?”, an agent without a memory layer has four bad options:
- Read everything — hundreds of thousands of tokens.
- Filename search — low cost, high failure rate.
- Query-time LLM — re-infer on every question; slow and expensive.
- Thin file wrapper — returns paths; agent still reads whole files.
The agent needs a decision layer before a reading layer. HCP formalises that split.
Design thesis
Memory is not reasoning. Prepare the files; return the source; hand only the relevant context to the model you already use.
Why “Hippo Campus”
The hippocampus encodes experiences, binds content to context, and retrieves from incomplete cues — it does not “think”; it makes thinking possible by supplying the right trace at the right time. A campus is an organised memory space: buildings (files), maps (chunks), directories (types), transit rules (scope, permissions, confidence). Agents navigate by cue, not by memorising every room.
What agents receive
When a question is asked, HCP returns short result cards: the most relevant passages, which file they came from, their page, cell, or timecode, and how confident the match is. Each result is designed to fit in a few hundred tokens, not tens of thousands.
When a short result is not enough, the agent can request more from the source. The default is a small, linked result; loading more is an explicit choice, not an automatic whole-file dump.
Local connection for AI tools
IDE agents and desktop assistants connect to HCP on the customer’s machine or private network. The searchable library is not sent through a Find and Seek cloud API. You keep your AI tool; Find and Seek supplies its memory.
Campus isolation
Each department, matter, or client can have its own search boundary. Permissions are checked before anything reaches an agent. Campus isolation is built and tested to six separate groups. The layer that maps an individual employee to a principal is not built — walls are drawn per group.
Limitations (honest)
Short result cards are the default, not a ceiling. Agents that need more can request it. A limited result may miss a detail that only a full-file read would catch — we publish those limits here rather than hiding them.
The bounded default applies when agents use Find and Seek to answer questions about connected files — not for general chat or queries that bypass it.
Find and Seek
Find and Seek implements the HCP memory plane: organise files ahead of time, return the source, and run approved analysis locally. Product overview on Product.