The most common complaint about AI agents in production is the same complaint customers have about junior hires: the agent asks the same question twice, repeats the same mistake, and doesn't remember what the team decided last week. The fix isn't a longer context window. The fix is shared memory built into the platform.
What "memory" actually means
When we talk about memory for AI agents, we mean three different things that vendors mix together:
- Conversation memory: the context of the current exchange. The agent remembers what was just said. Any modern LLM handles this with a context window.
- Session memory: the context that persists between turns within a session. The agent knows it's about to send a follow-up email when it just drafted the first one.
- Domain memory: the context that persists across sessions — the customer's history, the team's decisions, the agent's own corrections. This is where production agents fail.
Conversation memory is a solved problem. Session memory is mostly solved. Domain memory is where the work is.
What domain memory needs to persist
An AI agent working inside a business needs to remember:
- Record identity: the contact it dealt with yesterday is the contact it should reason about today. This is the most basic and the most-broken in production.
- Decision history: the agent decided last Tuesday that a certain customer should be escalated to a human. It should remember that decision.
- Human corrections: when a human rejected the agent's draft, the agent should learn from that rejection — not repeat the same draft tomorrow.
- Cross-agent context: the agent that drafted the email and the agent that schedules the meeting should agree on which customer they're talking about.
Why this is hard
Three reasons most vendors ship agents without good memory:
Vendor boundaries
The agent that lives inside the CRM has no access to the agent's history inside the support inbox. They're two agents from two vendors. From the customer's perspective, they look like one team. From the platform's perspective, they're strangers.
Data model absence
For agents to share memory, they need a shared data model for the things they're remembering about. That's a database. Vendors who don't operate a database can build a vector store but not a record of decisions. Decay and drift are inevitable.
Memory governance
Memory that the human team can't see, can't correct, and can't delete is a compliance problem. The agent that "remembers" a credit-card number in its context window is a PCI violation waiting to be reported. Memory has to be inspectable and governable, like every other piece of production data.
How we build it at KaryaFlow
Our agents share three layers of memory:
The platform's record store
Every fact the agent learns about a customer is written to the same record store the human team reads. The agent doesn't have a secret copy. It doesn't have a parallel database. The agent's "memory" of who the customer is and what's happened with them is just the CRM record, with the agent as one more reader and writer.
The agent's session transcript
Every interaction the agent has with a user is stored as a transcript — prompts, tool calls, results, observations. The transcript is inspectable by the team that owns the agent. Sensitive data is redacted according to the same policy that applies to the rest of the platform.
The team's corrections
When a human rejects the agent's output, the rejection is captured with the agent's reasoning. The next time the agent is asked to perform the same task, the rejection is in the context. The agent learns at the team level, not just the individual session level.
The practical test
A simple test for whether an agent has real memory: ask it the same question a week apart. If the second answer doesn't reflect what it learned from the first interaction, the agent has no domain memory. If the second answer contradicts what the human team decided last week, the agent doesn't have access to the team's decisions. If the second answer can't be corrected by the team, the memory isn't governable.
Vendors who built their agents on a real platform answer this test positively. Vendors who bolted AI onto an existing product answer it with caveats.
Read about our agent developer experience or talk to our team about what production-grade agent memory looks like.