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Orchestration Engine · The Platform · By Assay

Every AI agent you run answers from its own stale memory.

The AI SDR quotes a price you changed in March. The support copilot cites a feature you deprecated. The RAG bot invents a cert you never held. Each one answers from whatever it was trained on or last indexed, an older, unsourced version of the truth. The Orchestration Engine points every agent at one endpoint, Assay MCP, so they read the live claim instead, scoped and logged. Stop fine-tuning on yesterday. Point them at the canon.

Run the audit → See the canon
One endpoint · OAuth 2.1 · scoped per agent · per-claim audit log
mcp.assay.wiki/your-org
OAuth 2.1
AI SDR · 11x pricing, features
Support Copilot features, security
Agentforce full canon
RAG knowledge bot claims
Audit tail · live
14:02:11 · sdr-agent read claim:pricing.seat@v7 · sourced
14:02:09 · copilot read claim:soc2.status@v3 · sourced
14:02:04 · ragbot read claim:regions.ga@v5 · sourced
How It Works

The same question. Its own memory, or the canon.

Point any agent at one endpoint and the difference is the whole product. Off its own training set, it improvises. On the canon, it cites. Pick an agent and ask it the question your buyer will.

Your AI agents
Reads
Orchestration
Engine Assay MCP
Answers
The same question, two ways
Buyer asks: What does a seat cost?
× off its own memory
"Plans start at $499 per seat."
answered from training data · Jan 2024
✓ on the canon
"From $720 per seat, billed annually."
canon · verified 3d ago
What makes it different

The agent is current because it never memorized.

Retraining and re-indexing are how you keep a copy fresh. Assay keeps no copy. Agents read the live claim at answer time; connected systems receive every canon change the moment it happens, scoped, previewed, and written to a log you can hand your GC.

01
Assay MCP
One Model Context Protocol endpoint, mcp.assay.wiki/your-org. Every agent reads from the canon instead of its training set or a stale vector store. Wire it once.
02
Push to every system
Change the canon once; Orchestration pushes it out, to Webflow, HubSpot, Salesforce, Slack, AI SDR prompts. Semantic propagation, not Zapier plumbing: it knows a pricing change from a contact-field edit.
03
Scoped per agent
OAuth 2.1. Each agent sees exactly the claims it should, the SDR reads pricing, not the roadmap; the support bot reads features, not pricing experiments.
04
Dry-run + kill switch
Preview the before-and-after on every destination before you commit. Field-level opt-in. The kill switch is one click, so nothing ships that you didn't see first.
05
Per-claim audit log
Every read and every push is logged: which agent or system, which claim, which version, when. The day your GC asks what the AI told a customer, you have the receipt.
06
Live, never trained
Canon changes; the next read reflects it. No re-training, no re-indexing, no embedding refresh. There is no stale copy to go stale.
The cost of unsupervised agents

What every agent's private truth costs you.

7+
AI agents touch a typical enterprise GTM motion, SDR, copilot, RAG, CRM agent, each carrying its own context. composite · our audits
~27%
of AI-generated commercial answers contain a claim that's outdated or can't be traced to a source. composite · our audits
0
re-trainings to keep every agent current. The canon updates; the next read is already right. Assay platform
One last thing

Your agents will answer a buyer in the next hour. On whose version of the truth?

The free audit shows you what your AI surfaces are saying today and where they've drifted from the canon. 90 seconds, in your private workspace.

Run the audit → See the platform