Thread by @tyler_agg

how to give an AI agent memory to make it smarter over time:

most AI system treats every conversation like it's meeting you for the first time

for example a customer first complains about a broken chair, then the AI responds like it's a new issue

but the same customer complains about another broken chair 2 months later

but in most cases an AI system still treats it like a new convo and acts like it's never happened before

what an AI with memory would do:

"Hello David, following up on your previous case from August where you experienced issues with an ergonomic chair, I sincerely apologize this is happening again. We appreciate your continued trust since 2023..."

it references past interactions and treats customers like actual humans

the difference is night and day

without memory: generic responses that ignore history

with memory: personalized responses that acknowledge patterns and build relationships

this applies whether you're doing customer support, managing clients, etc

here's the simple memory system:

> filter what's worth remembering step
> organize the information
> inject relevant history back into conversations

let me explain each of them...

step 1: filter the noise

not every interaction is worth storing

someone asking "what's 2+2?" - ignore

someone reporting a real product issue - store it

you want to capture actual relevant conversations rather than random garbage that clogs up your database

step 2: organize everything you store

> categorize the type of interaction: shipping, refunds, product issues
> summarize the conversation in a few sentences
> note if the customer was pissed off or if human intervention is needed

this gives you clean, searchable information for later

step 3: feed relevant history back to the AI

when someone reaches out, grab:
> their profile info (member since when, past purchases)
> recent interactions with your team
> any similar past issues

you need an actual system to store interactions - even a simple database or spreadsheet works to start

so that you can inject all this into your prompt so the AI has full context

the key is making information retrievable

you can't just dump everything in a folder and hope the AI figures it out

organize conversations by customer, date, issue type, and outcome

so you can find what you need when you need it

here's where it gets powerful: when someone reaches out, you need to grab three types of information

> their profile (member since when, past purchases, location)
> recent interactions (last 5 conversations with your team)
> similar past issues (this is where embeddings come in)

embeddings let an AI find similar conversations automatically

they turn text into numbers that capture meaning

"missing screws in my chair" and "chair came with broken parts" get similar numbers because they mean the same thing

this is how the AI connects different words that have identical meanings

what this allows us to do:

customer complains about a wobbly chair today

we automatically find their complaint about "unstable furniture" from 6 months ago

even though the exact words are different…

embeddings help the AI see they're related issues

so when generating a response, we can retrieve:

> customer profile: "member since 2023, bought 4 items including chairs"
> recent history: "last contacted us about shipping delays"
> similar issues: "previous chair defect complaint from August"

and now it has full context on the specific customer

to recap the AI memory system:

> filter and store relevant interactions in a database
> organize by customer, issue type, and urgency level
> use embeddings to find similar past conversations
> inject profile + recent history + similar issues into your prompt

this turns a generic AI agent into one with actual memory on all past interactions