Memory-augmented agent
Assistants that remember customers and learn procedures across sessions without stuffing history into every prompt.
Use it when
Relationship-style assistants (support, sales, coaching, personal agents) where context from previous conversations changes the right answer, and where long context alone is too slow and too expensive.
The parts, top to bottom
across every level
Hover or tap any part to see what it does. The light shows the order a request moves through.
What happens, in order
- 1Session starts with short-term state
- 2Retrieve relevant long-term memories
- 3Agent reasons and acts
- 4Extract salient facts from the exchange
- 5Consolidate into semantic, episodic and procedural memory
- 6Expire and audit memories by policy
What we typically build it with
Trade-offs
A dedicated memory layer beat full-history prompting on accuracy, latency and token cost in Mem0's evaluation, but memory can be contaminated or stale, so consolidation rules, expiry and auditability matter as much as recall.
Problems this architecture solves
Generic problem statements with the flow and the outcomes the industry has documented.
Support queues that never shrink
Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.
How the system works
- Customer message
- Intent & policy check
- Retrieve from your docs & orders
- Answer or act via tools
- Escalate with context
Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release
ArchitectureSales conversations stuck on personal phones
Orders, questions and promotions run through WhatsApp with no history, no automation and no way to see what drove revenue.
How the system works
- Ad or QR opens chat
- AI agent answers from catalogue
- Cart & payment in chat
- Order synced to store
- Campaign & follow-up
Outcome: 53% of retailers already automate messaging on the WhatsApp Business Platform (Infobip), and the same grounded-agent pattern turns that channel into a store, a support desk and a campaign tool with attribution. Infobip WhatsApp statistics
ArchitectureOther agentic patterns
Single agent with tools
One model, a toolbox, and a loop that decides what to call next.
Use it when: A conversation or task needs judgement about which system to consult and in what order: customer queries, sales qualification, commerce in chat, internal copilots.
Flow
- 1User or event
- 2Agent reasons about the goal
- 3Calls a tool (search, CRM, calendar)
- 4Observes result
- 5Repeats until done
Supervisor multi-agent system
An orchestrator plans; specialised workers execute; results are merged.
Use it when: Multi-step processes that touch several systems or skills: research and due diligence, back-office case handling, candidate pipelines, anything where one agent's context would overflow.
Flow
- 1Task arrives
- 2Supervisor decomposes into steps
- 3Workers run in parallel with their own tools
- 4Results validated & merged
- 5Human checkpoint if needed
Let's build intelligent systems that drive growth
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