Even the most advanced AI models struggle with one persistent issue in customer support: context collapse. This happens when a model loses track of the conversation’s history, especially in long, multi-agent tickets. The result? Repetition, confusion, and frustrated customers.
This isn’t about token limits. It’s about how support workflows—spanning days, channels, and systems—overwhelm stateless LLMs. Without memory or orchestration, even the smartest AI can’t keep up.
As AI becomes more embedded in customer service, its limitations are becoming more visible—especially in complex, long-running support tickets. One of the most critical and often overlooked issues is context collapse: when the AI loses track of the conversation’s history, leading to broken continuity and poor customer experiences.
In customer support, context collapse refers to the breakdown of continuity in AI-driven conversations—especially in long, multi-step tickets. It’s not just about exceeding a model’s token limit. It’s about the AI losing track of what’s already been said, what matters now, and what’s changed since the last interaction.
Unlike generic context window issues, this collapse is workflow specific. It happens when tickets span multiple agents, channels, or days—where the AI lacks memory of prior exchanges or fails to prioritize the right details. The result is a model that responds as if each message is a fresh start, ignoring the ticket’s history.
Long tickets introduce complexity that overwhelms stateless LLMs. These tickets often involve:
LLMs process each prompt independently. Without engineered memory or structured context, they can’t reconcile these shifts. This leads to fragmented responses, missed details, and repeated questions.
From the customer’s side, context collapse feels like déjà vu. They’re asked to repeat themselves. Promises are forgotten. The conversation feels disjointed. This erodes trust and patience—two key drivers of CSAT. When customers feel like they’re starting over every time, satisfaction drops fast.
Long support tickets aren’t just longer—they’re structurally different. They span time, tools, and teams, creating a fragmented experience that’s hard for AI to follow. To understand why even advanced models like ChatGPT or Gemini struggle, we need to break down what actually makes these tickets so complex.
Long tickets rarely follow a straight path. They often involve multiple agents, bots, and communication channels—email, chat, even phone—interacting across a single thread. Each handoff increases the risk of losing key context. Without a unified view, AI models struggle to maintain continuity, especially when escalations introduce new layers of complexity.
Support tickets that stay open for days or weeks become brittle. Time gaps between responses cause both humans and AI to forget earlier details. For LLMs, which don’t retain memory between calls, this decay is immediate. Unless context is reintroduced manually or through engineered memory, the model treats each reply as a standalone event.
Support data lives in silos—CRM records, chat logs, email threads, internal notes, and knowledge bases. These systems rarely sync in real time. When an LLM is asked to respond without access to the full picture, it relies on partial or outdated context, increasing the chance of errors or irrelevant replies.
This is where understanding context in ChatGPT vs Gemini becomes critical. While both models are capable of handling long-form input, their approaches to context retention differ. Gemini, for instance, emphasizes multi-modal memory and cross-session continuity, whereas ChatGPT (especially in its stateless API form) requires explicit context injection. In long-ticket workflows, these differences can significantly affect how well each model maintains coherence across fragmented inputs, and CoSupport AI emphasizes that.
To understand why even high-token models like GPT-4-Turbo or Gemini 1.5 still falter, we need to look at the technical architecture behind their memory and reasoning.
LLMs operate within strict token limits. When a support ticket exceeds that limit, the model must truncate—cutting off earlier parts of the conversation. This often removes crucial context, especially in long, multi-turn threads. According to recent evaluations in 2025, models with extended context windows (128k+ tokens) still struggle with long-range coherence in real-world support workflows and relevance ranking.
Fine-tuning helps models specialize, but it doesn’t give them memory. LLMs don’t retain information between sessions unless explicitly designed to do so. Without engineered memory—like retrieval systems or persistent embeddings—each prompt is a reset. This makes it nearly impossible for the model to “remember” what happened earlier in a long ticket unless that context is re-fed every time.
Even when context is present, LLMs may misinterpret what matters. They often prioritize syntactic similarity over semantic importance. For example, a model might latch onto a recent refund request while ignoring a critical earlier message about account access. This misalignment between user intent and AI recall is a core driver of context collapse in support environments.
Context collapse isn’t a failure of intelligence—it’s a failure of design. LLMs like GPT-4-Turbo, Gemini 1.5, and Claude Opus are powerful, but they’re stateless by default. Expecting them to oversee long, fragmented tickets without engineered memory or orchestration is unrealistic. The solution isn’t to replace the model—it’s to redesign the workflow around it.
Fixing context collapse starts with understanding the ticket lifecycle. By organizing conversations around user intent, layering in memory-aware architecture, and empowering agents to guide key moments, support teams can build AI workflows that maintain clarity and continuity. The future of AI in support isn’t about smarter models—it’s about smarter orchestration.
