N71

Give all your AI agents one shared context

Productivity

Developer Tools

Artificial Intelligence

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The "every agent call is scoped, cited, and authorized" line caught my eye. Is that scoping configurable per agent/tool (e.g. keeping HR or finance data out of a general-purpose coding agent), or is it uniform across whatever you connect?

Explaining the same background over and over to every new assistant gets old fast, so having them all caught up already really appeals to me. It feels like the missing piece that makes these tools genuinely usable day to day.

Promising direction. The hard product edge I would test is revocation: if a document, repo access, or customer thread is removed, can downstream agents still cite or answer from cached graph facts?

Shared context is only safe if permission changes and deletions propagate as first-class events, not just as better retrieval later. I would love to see the audit view expose the current answer, source freshness, and whether any source behind it has since been revoked.

plugged it into a few of my agent setups and the shared context thing actually works, no more re-explaining the same project state every time i open a new chat

Shared context across agents sounds exactly like what the space needs. I tried it with a few MCP-connected tools and noticed new chats actually picked up where I left off instead of starting from zero, which is a relief after fighting that for months.

Plugged it into three of my daily agents and the shared context thing actually works. I was surprised how instantly they picked up on decisions I made earlier in a different tool without me re-explaining anything.

one thing I didn't see covered yet - what happens to everything the graph already learned from a tool once you disconnect it. does that history get purged from the graph, or does it stick around as context agents can still read even though the source is gone and can no longer be re-verified

plugged it into my notion and slack setup and it actually kept track of shifting priorities without me babysitting it, which is more than i expected from a context layer.

How does the knowledge graph stay accurate when multiple agents are editing overlapping context at the same time, do you handle conflicts or does the latest write just win?

Plugged it into my setup and asked three different agents about a project update — they all pulled the same current info without me retyping anything. The shared context piece feels like the actual unlock.

Re-explaining my project context every time I switch between Claude and another agent is genuinely one of the most annoying parts of the current workflow. A living knowledge graph that updates automatically when things change is the right direction — static docs go stale immediately. How does it handle conflicting information across sources when two tools say different things about the same project?

the living knowledge graph over MCP is genuinely clever. tired of starting every chat from zero and re-explaining context. finally feels like the agents actually talk to each other instead of me playing middleman

How does the knowledge graph stay accurate when multiple agents are writing to it at the same time, and is there any conflict resolution if two tools update the same fact differently?

how does it decide what counts as a meaningful change worth updating the graph for vs just noise from tool activity

Hooked up my main work chat and a few agents, and the shared context finally clicked for me. It is wild seeing answers pull in what I sorted out three tabs ago without me copy pasting anything.

Curious how this handles conflicting info when two agents surface different versions of the same fact at the same time, does the graph reconcile automatically or flag it for you to resolve?

That's the half most memory layers skip, so good to see writes carry receipts. The catch we ran into: provenance only pays off if the reading agent actually looks at the score, and most of them just grab the top fact and run. Does N71 down-rank or filter low-confidence facts in the MCP response itself, or hand back the fact plus its score and trust each agent to discount it? Enforcing it server-side was the only thing that stopped one shaky write from spreading for us.

The shared-read part is the easy win. The thing that bit us building a shared agent memory was write trust: one agent writes a stale or wrong fact and now every other agent confidently inherits it, so a single bad extraction quietly poisons the whole graph. Curious how N71 handles that, do writes carry provenance and confidence so a downstream agent can discount a shaky fact, or is a write just a write once the broker accepts it?

Hey, Can multiple agents access the same context simultaneously without conflicts?