As we build stepback.dev, one of the most fascinating engineering challenges is context management. When you branch a conversation, how do you preserve meaningful context without overwhelming the model? When branches merge, how do you reconcile divergent histories?
We've been researching how leading AI companies approach these problems. Recent engineering blogs reveal that the industry has moved beyond simple "large context windows" — sophisticated architectural strategies like "context anxiety" management, shadow workspaces, and just-in-time retrieval are now the norm.
Anthropic: Just-in-Time Retrieval
Anthropic's strategy focuses on minimizing the "cognitive load" on the model by keeping the context window clean.
Just-in-Time Context Loading: Instead of dumping an entire codebase into the context window (which can confuse the model), Claude Code uses lightweight references — file paths, function names — and retrieves full content only when necessary. This mimics human cognition: using an "index" to find information rather than memorizing everything.
Contextual Retrieval: Standard RAG often fails because small chunks lose meaning when isolated. Anthropic solved this by generating a concise explanation for each chunk before embedding it. For example, a chunk containing def calculate_metric(x)... is stored with added text: "This function calculates revenue for the Q3 dashboard component".
"The Model Context Protocol (MCP) allows models to 'connect' to data sources and fetch only the relevant 'active' context dynamically, rather than relying on a static context snapshot."
Cognition: Managing "Context Anxiety"
The team behind Devin identified a phenomenon they call "Context Anxiety" — a behavioral change where model performance degrades as it senses the context window filling up.
When nearing the context limit, models become "anxious," taking shortcuts or leaving tasks incomplete. To counter this, Devin proactively summarizes its own progress into external files (like CHANGELOG.md or SUMMARY.md) to "offload" memory before the context fills up.
Single-Threaded Continuous Context: Unlike multi-agent systems where context is fragmented across different "workers," Cognition intentionally uses a single-agent architecture. This ensures that "implicit decisions" made in previous steps are preserved in a unified history, preventing the "amnesia" that occurs when handing off tasks between agents.
Cursor: The Shadow Workspace
Cursor uses a hybrid approach that combines fast, local context with a "Shadow Workspace" to validate the model's understanding before you even see it.
Shadow Workspace: Cursor maintains a hidden, parallel version of your codebase. When the AI proposes a change, it first applies it in this "shadow" environment to check for compiler errors or linting issues. The context isn't just textual — it's functional, validated by a real compiler before being presented to the user.
Speculative Execution: Cursor doesn't just look at what you typed; it predicts where you'll go next. It uses "speculative execution" to pre-generate likely edits and "Model Orchestration" to mix fast models (for instant cursor jumps) with slower, smarter models (for complex logic).
Surgical Context via Embeddings: Cursor indexes your entire repository into vector embeddings but avoids "context chaos" by being surgical — pulling only files that are semantically relevant to the active task, rather than flooding the window.
What This Means for stepback.dev
Building a branching conversation tool means we need to think carefully about these strategies:
- Branch context inheritance: When you branch, what context travels with you? JIT retrieval suggests we should carry references, not full content.
- Merge reconciliation: When branches merge, how do we reconcile divergent explorations? Cognition's "unified history" concept is instructive.
- Context anxiety prevention: Long conversations with many branches could trigger anxiety behaviors. Proactive summarization might be the answer.
- Validation before presentation: Cursor's shadow workspace concept could ensure branches are "healthy" before users commit to them.
We're actively exploring these patterns as we architect stepback.dev's backend. If you're working on similar problems or have insights to share, we'd love to hear from you.
Summary of Industry Approaches
| Company | Core Strategy | Unique Feature |
|---|---|---|
| Anthropic | Just-in-Time Retrieval | Contextual Retrieval (adding context summaries to chunks) |
| Cognition | Anxiety Management | Proactive Summarization (offloading memory to files) |
| Cursor | Functional Context | Shadow Workspace (validating changes with a hidden compiler) |
This is just the beginning of our exploration. As we continue building, we'll share more insights about how these strategies translate into a branching conversation architecture.
— The stepback.dev team