Does Cassidy AI Remember Between Runs? What Actually Persists

Short answer: partly. Cassidy AI persists your knowledge base, your workflow definitions, and your run history. But the run itself starts fresh every time. Nothing the workflow learns during one execution is automatically available to the next one. That distinction matters more than it sounds, so here is the honest breakdown.

What Cassidy AI actually is

Cassidy is an AI automation platform built for non-technical teams. You build multi-step AI workflows without writing code: customer support triage, RFP responses in your brand voice, CRM updates from sales calls, prospecting against an ideal customer profile, surfacing client stories in Slack. It connects to more than 50 tools, pulls from live sources like SharePoint and Slack, and runs anywhere your team works: its own chat, Slack, Teams, browser and Office extensions, or an API.

The company raised a $10 million Series A in 2025, and enterprises like NTT Data use it to automate work that used to take hours of manual effort. It is a serious platform for business automation. The question is not whether Cassidy is good at what it does. The question is what, exactly, it remembers.

What persists between runs

Several things do survive from one execution to the next, and they are worth listing because they are genuinely useful:

The knowledge base. Cassidy lets you connect authoritative sources, organize them into collections, and set approved answers for sensitive topics. This knowledge layer is curated by you, updated by you, and available to every workflow every time it runs. It is the platform's strongest form of persistence.

The workflow definition. Your instructions, variables, model choice, error paths, and human-review gates are all saved. Version them, improve them, and the workflow runs the new version next time.

Run history and audit trails. Every execution leaves a record: what happened, what the agent produced, where it failed. You can inspect this. Cassidy's own deployment guidance says to operate these automations like software: review logs, check source freshness, version changes, disable stale automations.

That is a solid foundation. But notice what is missing from the list.

What does not persist

None of the above is memory in the sense your automation actually needs. Consider a support-triage workflow that runs every morning:

• Monday, it miscategorizes a batch of tickets because a new product name collides with an old category. You correct the output in Slack.
• Tuesday, it makes the same miscategorization. Same prompt, same knowledge base, same definition. Your Monday correction lives in the run log, where no workflow can read it.
• Wednesday, same thing. The workflow is not stubborn. It is amnesiac. Each run starts from the same configuration with zero awareness that this exact mistake was corrected twice already.

This is the core gap: the knowledge base only changes when you change it. Corrections, discoveries, preferences, and patterns that emerge during real execution never flow back in on their own. The run history is a record for humans, not memory for the agent. An agent that cannot read yesterday's corrections to do better today is starting from zero every morning, no matter how good its knowledge base is.

The same applies to chat deployments. An assistant that handles fifty conversations in Slack has no notion of what it learned in conversation one through forty-nine. Every conversation is a blank slate with the same configuration.

Why this distinction matters for scheduled automation

Scheduled workflows are where the gap compounds. A one-off agent that starts fresh is a minor annoyance. A daily workflow that starts fresh is a daily tax: the same correction, applied by a human, morning after morning. The failure mode is quiet and expensive. Nothing breaks. No alert fires. The automation just quietly underperforms its potential forever, because the feedback loop between "the workflow got this wrong" and "the workflow knows better now" runs through a human being who has to remember to update something.

Cassidy's own best practices recognize this pressure. Its deployment guidance tells teams to insert human controls, review outputs, and continuously curate the knowledge layer. That is sound advice, and it is also a confession: the platform knows the knowledge layer decays, and the only refresh mechanism is you.

What operators do today

Teams working around this gap generally land on one of three patterns:

1. Manual knowledge-base curation. After every interesting run, someone updates the sources, tweaks instructions, or adds an approved answer. Works, but it is a human memory task bolted onto an automation platform. The person who remembers to do it is the real memory layer.
2. Prompt stuffing. Operators paste the last few corrections or a summary of recent learnings into the workflow instructions before big runs. This turns instructions into a junk drawer and still requires a human to keep the drawer current.
3. Log review rituals. Weekly or monthly sessions where someone reads run histories and translates them into workflow changes. Better than nothing, but the latency between a mistake and the fix is measured in weeks.

All three share the same flaw: a human carries the memory that should belong to the agent.

The fix: a memory layer the agent carries itself

The answer is not a better knowledge base. A knowledge base holds curated facts. What your workflows need is memory: what happened, what was corrected, what was decided, what worked, stored per run and recalled when relevant, without a human translating it.

That is what Vilix AI provides: one persistent memory layer for your agents, hosted in the cloud so there is no infrastructure to run and nothing to maintain. The same memory follows your agent across every tool and every device through MCP, so a correction made in Slack on Monday is available to the scheduled workflow on Tuesday. Vilix AI stores full conversation history, not just extracted facts, which means the real conversation can be revisited anytime, not just the bullet points someone distilled from it.

Getting started costs nothing: the free plan is free forever, and the Pro trial gives you seven days with no credit card required. Your data stays portable, so you can export everything or delete it whenever you want. There is nothing to lose by giving your agents the memory they keep proving they need.

Bottom line

Cassidy AI remembers your knowledge base, your workflow definitions, and your logs. It does not remember what it learned while running. For occasional automations, that is fine. For scheduled workflows that run daily, it means the same corrections, applied manually, on repeat, forever.

Your automations deserve better than a memory that only updates when you do. Give them one that updates itself.

Learn more: vilix.ai