An AI agent is only as good as the context it works with.
At the start of a conversation that’s easy. But real conversations don’t stay short: a customer negotiates over days, an onboarding stretches across weeks, a WhatsApp thread accumulates hundreds of messages. If the agent re-reads everything on every turn, the context window fills with noise, costs climb, and the model’s attention dilutes. The detail that matters ends up buried among hundreds that don’t. The industry has a name for this silent degradation: context rot.
Throwing a bigger model at the problem doesn’t fix it. Managing the context does.
A great agent is a model plus a harness
The model is the engine. What makes an agent reliable in production is the harness built around it: the tools it can call, the knowledge it can search, the guardrails it operates within and, critically, how its context is managed. What enters each turn, what stays, what gets condensed, what gets remembered.
That harness is what Grovity lets you build for your agents: prompts, tools, knowledge bases, multi-agent teams, evals, and now memory.
Two layers of memory
Rolling summaries. The agent keeps a window of recent messages verbatim. As the conversation grows, older messages are automatically folded, in batches, into a living summary that travels with the conversation. The agent always works with sharp recent context plus a condensed version of everything before it. A conversation can run for weeks without dragging its entire history on every turn, and without losing the thread.
User memory. Some things shouldn’t live in one conversation: they belong to the person. When the agent detects durable information (a name, a preference, a restriction, an agreement), it saves it as a memory tied to that user. Memories are shared across all of your company’s agents, so the next conversation, on any channel and with any agent on your team, starts already knowing the customer.
You decide how it works
Memory is configured per agent, right from the portal (Setup → Memory): how many recent messages stay verbatim, how often summaries are generated, how many memories are kept and injected. Each layer switches on independently, so you can tune the harness to how your operation actually converses.
Why it matters
- Extended conversations that hold up. Sales cycles, support cases and onboardings that span weeks, without the degradation of an overloaded context.
- Customers who are remembered. No more asking for the same information twice. The agent picks up where the relationship left off, not where the last message did.
- Sharper agents at lower cost. Less noise in context means better answers and fewer wasted tokens on every single turn.
Memory, tools, knowledge, testing: the harness is what turns a language model into a dependable member of your team. Now that harness remembers.
Want to see it on your own agent? Book a demo.