Token waste
Raw API responses pile up in the context window before the agent starts reasoning. It spends tokens cleaning data instead of answering.
CONTEXT STORE
One context layer for every agent, across every business system
A managed data layer that replicates, unifies, and indexes your business data before any agent runs. Queryable in milliseconds from the SDK, CLI, or any MCP client.
~/AIRBYTE-AGENT — TERMINAL
WHY AGENTS STRUGGLE IN PRODUCTION
MCPs give agents access.
They do not give agents a map.
Every answer becomes a live crawl across Salesforce, Zendesk, Stripe, and the rest. The agent burns tokens, hits rate limits, and can only find what it already knows to ask for.
Latency stacking
Every source is another round trip. Five systems means five sequential API calls, each with its own rate limits, before the agent can begin.
No map
APIs only answer questions you already know how to ask. The agent can't see what data exists or how it connects, so it never asks for what it doesn't know is there.
WHAT A QUERY COSTS
Your agent receives a query about Flowtech.
Here's what happens.
Without Airbyte5 systems = 5 live calls
01
Round trip. Renewal date buried in dozens of fields.
Salesforce API
02
Round trip. Rate-limited. Retry.
Zendesk MCP
03
Round trip. Completely different schema to parse.
Stripe API
04
Three raw payloads dumped in. Tokens burned before reasoning starts.
Gong MCP
05
Three different "Flowtech" records reconciled on the fly.
Linear MCP
Five round trips before the agent can begin to reason. Slow, expensive, and brittle the moment an API rate-limits or returns partial data.
With Airbyte1 search, pre-linked
01
context store
One query returns the unified Flowtech record, renewal, support, payments, already connected.
02
reason
The agent reasons immediately and answers.
One round trip. Fast, cheap, reliable.
How it works
The Context Store owns the knowing.
Agent Connectors own the doing.
READ & SEARCH
Context Store
Selected business entities are replicated into a managed, searchable index. Agents query unified context in a single call. No live crawl, no stitching.
WRITE & ACT
Agent Connectors
When an agent needs to act, Agent Connectors handle real-time reads and writes back to the source systems.
How they work together
Your agent discovers what it needs through the Context Store, then acts on it through Agent Connectors. It always knows where to look and always has a direct path to act.
the numbers
Better context. Faster agents.
Measured across Gong, Linear, Salesforce, Slack, and Zendesk connectors. Open source. Model-agnostic. Your context, your stack.
73%
fewer tokens on a single query
33%
fewer tool calls vs. native vendor MCPs
83%
cost savings on multi-source queries
USE CASES
What agents can do with the Context Store
Cross-system questions your team already asks, answered by agents against indexed context, with live actions when needed.
tickets = await zendesk.execute("tickets", "context_store_search", params={"query": {"filter": {"eq": {"status": "open"}}}}) invoices = await stripe.execute("invoices", "context_store_search", params={"query": {"filter": {"eq": {"status": "past_due"}}}})
# tickets (zendesk) [ { "id": 4892, "subject": "Checkout API failing", "status": "open" }, { "id": 4891, "subject": "Dashboard export empty", "status": "open" } ] # invoices (stripe) [ { "id": "in_1P8x", "customer": "Flowtech", "amount_due": 12480, "status": "past_due" }, { "id": "in_1P8y", "customer": "NorthPeak", "amount_due": 4200, "status": "past_due" } ] // ─── truncated ─────────────────────────────────────
HOW THE CONTEXT STORE IS DIFFERENT
Built for agents first.
If your agent answers single-turn questions over one system, a vendor MCP or a RAG pipeline is fine. The Context Store earns its keep when answers span systems and run in production loops.
Isn't this just a warehouse?
Warehouses centralize data for analysis. They aren't built for the millisecond, single-query retrieval agents need to reason in real time. The Context Store is built for operational speed, not historical reporting.
Isn't this just RAG?
RAG chunks documents and retrieves passages. The Context Store indexes structured business entities and their relationships, so an agent can ask about an account and get its deal, tickets, and payment state as one object.
ENTITY RESOLUTION
Sarah Chen in Salesforce is sarah@flowtech.com in Stripe is Ticket #4892 in Zendesk.
The Context Store unifies records across systems at the data layer. Your agent queries one structured entity instead of reconciling three fragments at runtime.
Deterministic entity resolution is on the roadmap. Today, the unified context layer already gets your agent to the right answer the vast majority of the time.
ONE LAYER, EVERY PATH
Every path queries the
same Context Store.
However your agents connect, they query the same unified context. Same entities. Same freshness. Same permissions. One context layer for your whole company.
FAQ
Didn't find your answer?
Please don't hesitate to reach out.
How is the Context Store different from connecting vendor MCP servers?
Vendor MCP servers expose one system at a time. Each call returns raw API payloads your agent must page through, rate-limit, and stitch together. The Context Store replicates a curated subset of entities from every connected source into one searchable layer, so agents query unified business context in a single call—with indexed search, cross-source queries, and automatic refresh.
Is the Context Store a vector database?
No. The Context Store is a managed, searchable replica of business entities from your connected sources—not an embedding store or vector index. Agents search structured entities and fields that Airbyte replicates from connectors, rather than retrieving semantically similar document chunks.
How much does the Context Store reduce token usage?
Measured across Gong, Linear, Salesforce, Slack, and Zendesk connectors, the Context Store reduces token usage by up to 80% compared to direct API calls (up to 90% for Zendesk) and cuts tool calls by up to 40%, because agents query pre-indexed context instead of paging live APIs into the LLM context window.
Can't we build this in-house?
You can stitch connectors, sync jobs, entity resolution, and search yourself—but that stack is the product. The Context Store ships curated replication, continuous refresh, indexed search, and the same MCP/SDK/CLI surface your agents already use, so teams spend time on agent behavior instead of maintaining a private data plane.
What happens when pre-materialized data is stale?
The Context Store refreshes on a recurring schedule from your connected sources. For reads that must be fresher than the last sync, agents can still call through Agent Connectors to the live system while keeping indexed context as the default path for search and multi-source questions.
Your agents do not need more tools. They need context.
Connect your systems once. Give every agent the same governed, searchable business context.