How to handle token limits in long AI support conversations

How to handle token limits in long AI support conversations

Customer support conversations are rarely linear. These conversations can sometimes go back and forth for an hour (or longer), and your customer probably only described the full problem initially during the first few minutes of the call or chat. But by minute 40, your AI agent is re-hashing the same solutions and doesn’t quite remember the problem to begin with. 

That’s a token limit problem. It doesn’t mean your AI is broken or even lacking. 

Every large language model (LLM) reads a conversation through a context window with a fixed budget of tokens it can process. Long, long support conversations eventually exceed that fixed budget, and when your AI agent can’t process the entire context to answer, it starts to cut things. Typically, it starts with the oldest part of the conversation, and that’s usually the most important part (where the customer described the problem).

Not all is lost, though. There are things you can do to handle token limits in long AI support calls, and certain platform capabilities can better handle context across extended (and repeat) conversations.

What happens when a support conversation hits the token limit

A token is a chunk of text, and a rough rule for English is about four characters per token. The context window is the maximum number of tokens a model can take in at once, and everything counts against it:

1. System prompt

2. Tool definitions

3. Retrieved knowledge

4. Conversation history

5. Responses the model generates

Support conversations burn through that budget fast. It’s not that they’re being inefficient, either—these conversations just take time:

• Voice transcripts run long: Every exchange on a phone call becomes a transcript turn, filler words and all.

• Tool results pile up: Order lookups, account records, and knowledge base results get appended to the history every time the agent calls a tool.

• Repeat contacts compound: A customer on their third contact this week brings two earlier conversations the agent should already know about.

When the total exceeds the window, the API rejects the request or the application drops content to make room. Most implementations cut the oldest turns first. In support, that's frequently the turn with the account number and the original complaint.

And you can see how that would completely derail a conversation.

Staying under the limit has its own costs, though. You pay for input tokens on every turn, and researchers have found that models use information buried in the middle of long contexts less reliably than information at the start or end (Liu et al., "Lost in the Middle"). 

Yes, a bigger window raises the ceiling, but you still resend the whole conversation on every turn.

4 ways to manage context (and reduce tokens) in long conversations

Fortunately, there are better ways to handle a conversation than re-reading the entire window every time. Here are a few tactics you can mix and match depending on the length and frequency of your customer conversations:

1. Sliding window

2. Rolling summaries

3. Retrieval from memory

4. Structured state

1. Sliding window

Keep the most recent turns and drop everything older. Token usage stays predictable and the logic is simple. Unfortunately, early facts fall out of the window, so you’ll need to pin the system prompt and any critical facts so they never get dropped.

2. Rolling summaries

Compress older turns into a summary while keeping recent turns verbatim. The overall gist survives, at the cost of an extra model call. It’s not perfect, though. Summarizers also tend to lose specifics like order numbers and dates unless you instruct them to keep identifiers intact.

3. Retrieval from memory

Store facts and past summaries outside the prompt, then fetch only the relevant ones each turn through search. Prompt size stays bounded regardless of how long the relationship gets. This approach needs an extraction step and a store to retrieve from.

4. Structured state

Track the facts you know matter as explicit fields, like account ID, issue type, and troubleshooting steps already tried. Inject them every turn. It's the most token-efficient option for known data, but it can't capture important details you didn't anticipate.

The best combination of approaches for support is:

• Structured state for known fields

• Sliding window for the live exchange

• Retrieval for everything from prior conversations

How Twilio handles token limits in long support conversations

Twilio handles long-conversation context by splitting it in two: 

1. Your agent manages the live turn history in its own code

2. Twilio Conversation Memory supplies compressed, relevance-ranked context from everything that came before. 

Here's how each piece works.

The live conversation stays in your code

Twilio Agent Connect passes each incoming message to a single handler along with the conversation session and any retrieved memory. Your handler builds the prompt and calls your model, so the trimming strategy for the current conversation is yours to pick, whether that's a sliding window, rolling summary, or both.

That design is deliberate. Twilio's guide to latency in AI voice agents warns that managed conversation history layers built for chat often add delays and rigidity in voice. Benchmark them early if you use one.

Conversation Memory swaps replay for retrieval

After a conversation ends, Conversation Memory extracts observations, short facts about the customer, and stores them on the customer's profile alongside conversation summaries. When a new observation contradicts an existing one, Twilio updates the existing observation with the most recent version, and discards contradictions that lack supporting detail. 

The profile stays current with no stale contradictions burning tokens.

During the next conversation, the Recall endpoint returns the most relevant memories using both semantic and lexical search. If you don't pass a query, Twilio infers one from the last ten conversation turns. You can narrow results further with date ranges and result limits.

The documented limits keep the payload predictable:

• Observations: Up to 4,096 characters each

• Conversation summaries: Up to 4,096 characters each

• Recall results: Up to 100 observations, 100 summaries, and 100 communications per call

• Recall query: Up to 1,024 characters

At roughly four characters per token, a single summary tops out near 1,000 tokens. Setting a result limit on Recall is effectively setting your context budget for history.

Agent Connect wires this in for you. With memory mode set to always, it retrieves memory context with each incoming message, and a prompt builder formats profile traits and conversation history into text you append to your system prompt.

Voice adds a latency constraint

On phone calls, ConversationRelay handles speech-to-text and text-to-speech and streams text turns to your agent over a WebSocket. Twilio's internal benchmarks put it at under 0.5 second median latency and under 0.725 second at the 95th percentile. Prompt size on voice is a latency question as much as a limit question, since the model has to process the prompt before it can start speaking.

Twilio accounts for that in how memory gets fetched. On messaging, Agent Connect calls Recall on every turn. On voice, you choose how often Recall runs, trading context freshness against response speed.

Handoffs skip the transcript

When a conversation escalates to a person, the human agent opens on an AI-generated summary and the conversation ID instead of scrolling a 40-minute transcript. It's the same compression principle, just applied to people.

Questions to ask about any Conversational AI platform's context handling

Whatever you build on, these questions show how a platform behaves when conversations run long:

• What gets dropped first? Ask what happens when a conversation exceeds the window, and whether the system prompt and key facts are protected.

• Who controls the trimming? Find out whether history management is configurable or a black box.

• How does memory reconcile conflicts? A customer who changed their address shouldn't have both versions competing for space in the prompt.

• What are the retrieval limits? Ask for documented bounds on what memory returns per call.

• How does voice change it? Ask how context retrieval affects response latency on calls.

Plan for the long conversations from the start

Long support conversations aren’t a failure or an accident. No, they’re where AI agents earn trust or lose it, and failure doesn’t have to be inevitable. 

• Decide your trimming strategy before launch

• Keep known facts in structured fields

• Pull history by relevance instead of replaying it

Do that, and the token limit stops being a ceiling you hit and becomes a predictable budget you set. Get this right, and you stay in control. For a deeper look at how persistent context works across conversations, see our guide to AI customer memory, or explore the Conversation Memory docs.

Frequently asked questions

What is a token limit in an AI support conversation?

Twilio-connected AI agents work within the token limit of whichever LLM you choose. It's the maximum number of tokens the model can process at once, covering the system prompt, conversation history, retrieved context, and its own response.

How do orchestration platforms handle token limits and context truncation in long support conversations?

Twilio keeps live turn history in your agent's code, where you choose the trimming strategy, and uses Conversation Memory to retrieve relevant observations and summaries from past conversations instead of replaying full transcripts.

What happens when a support conversation exceeds the context window?

Twilio Agent Connect leaves history management to your code, so the outcome depends on your strategy. Without one, the API rejects the request or the oldest turns get cut, often including the customer's original issue.

Does a bigger context window solve the token limit problem?

Not always. A bigger window raises the ceiling, and you still pay for and wait on every token you resend. Twilio's voice AI guidance favors lightweight models tuned for fast first tokens, which makes compact prompts matter.

How much context does Twilio Conversation Memory return per turn?

Twilio Conversation Memory's Recall endpoint returns up to 100 observations, 100 summaries, and 100 communications per call, with each observation and summary capped at 4,096 characters. Date ranges and result limits narrow it further.