How are you storing long-term context for agents?

Anyone else feel like most AI agents + automations are just… fancy goldfish? 

They look smart in demos.
They work for 2–3 workflows.
Then you scale… and everything starts duct-taping itself together.

We ran into this hard.

After processing 140k+ automations, we noticed something:

Most stacks fail because there’s no persistent context layer.

• Agents don’t share memory

• Data lives in 5 different tools

• Workflows don’t build on each other

• One schema change = everything breaks

It’s basically running your business logic on spreadsheets and hoping nothing moves.

So @tadeas_marek @matous_kralik and team built Boost.space v5, a shared context layer for AI agents & automations.

Think of it as:

• A scalable data backbone (not just another app database)

• A true Single Source of Truth (bi-directional sync)

• A “shared brain” so agents can build on each other

• A layer where LLMs can query live business data instead of guessing

Instead of automations being isolated scenarios…
They start compounding.

The more complex your system gets, the more fragile it becomes, hence you need a shared context for your AI agents and automations. 

What are you all using right now as your “source of truth” for automations? Airtable? Notion? Custom DB? Just vibes? 😅