Overview
Cline has detailed a new feature called Focus Chain, described as a context-forward orchestration method for single tasks in its coding agent. The announcement, published by Kevin Bond on August 15, 2025, outlines a planning-then-execution model meant to curb goal drift in long-running sessions by keeping an explicit plan inside the model’s working context. The post also introduces a complementary “deep-planning” workflow that splits work into a planning phase and a follow-on implementation phase, with the same plan carried forward between stages.
Why context size alone has not solved drift
The post frames Focus Chain against a documented challenge in large language models: as prompts grow, models often lose track of earlier details even when context windows are larger. Research has reported a “Lost in the Middle” effect—a U-shaped performance curve where information at the beginning or end of a context is recalled better than material embedded midstream. The post cites analyses and studies discussing degradation in long contexts and the need for careful context management:
- Evaluations noting quality drops as context fills, including write-ups on long-context benchmarks and the “Lost in the Middle” phenomenon (for example: https://onnyunhui.medium.com/evaluating-long-context-lengths-in-llms-challenges-and-benchmarks-ef77a220d34d?ref=cline.ghost.io and https://ar5iv.labs.arxiv.org/html/2307.03172?ref=cline.ghost.io).
- Observations that extending windows—now up to 1M+ tokens—does not eliminate degradation at high lengths (e.g., https://arxiv.org/abs/2503.20589?ref=cline.ghost.io and a demonstration noting GPT‑4 accuracy declines beyond 64K of a 128K window: https://www.youtube.com/watch?v=KwRRuiCCdmc&ref=cline.ghost.io).
- Commentary from IBM Research underscoring information overload in larger windows (https://research.ibm.com/blog/larger-context-window?ref=cline.ghost.io). The post characterizes drift as a product of attention and window constraints: as tokens accumulate, earlier rationale can fall outside the active window, increasing the risk of repeated work or contradictions without explicit anchoring.
Cline’s context-forward stance
Cline’s write-up argues for task-aware, narrative-driven context over broad, passive retrieval. It states that indiscriminately chunking repositories and relying on vector similarity can surface tangential code and crowd out critical details. The post distinguishes between high-value tokens—such as requirements, critical code paths, and decisions—and low-value tokens like redundant snippets or tangential comments. It contends that broad retrieval tends to bloat prompts with low-value content, raising cost and latency while worsening outputs. Cline’s approach attempts to foreground high-value material and keep it where the model can act on it.
How Focus Chain works
Focus Chain is presented as a lightweight but persistent plan that travels with the agent through the task:
- At the outset, the agent drafts a numbered, step-by-step plan—the chain—for the requested task.
- The plan is kept inside the working context and is explicitly referenced at each step.
- The same model updates the plan as work progresses; completed items are marked and remaining steps are revised as needed.
- The evolving plan becomes part of the prompt, acting as a continuously refreshed anchor of past actions and upcoming goals.
According to the post, this explicit plan reduces the likelihood of re-doing steps or contradicting earlier decisions, by keeping the model’s “what and why” visible despite long horizons. The write-up claims measurably better outcomes on complex tasks, though it does not publish benchmarks.
“Deep-planning” workflow
The accompanying workflow, called deep-planning, is described as a two-stage process designed to exercise Focus Chain:
- Planning: the agent explores the codebase, reads relevant files, and drafts a comprehensive implementation plan for a feature or fix.
- Execution: a second task implements changes using the plan from the first phase as a constant guide.
Cline states that carrying the plan into the execution phase keeps the work aligned with the original design, even when generation spans very long sessions—described in the post as potentially “millions of tokens” of output. The post positions this as one example, suggesting the approach generalizes to other complex tasks that benefit from a persistent, structured context.
What is and is not specified
- The announcement emphasizes anchoring via an explicit, updated plan as the core technique, rather than relying on broad retrieval pipelines.
- The post asserts improved coherence on long-horizon tasks but does not include quantitative evaluations or public benchmarks.
- Availability timing beyond the post date and any pricing details are not disclosed.
- The post notes that other coding agents have shipped similar ideas but argues Focus Chain’s design is notably context-forward.
References and further reading
- “Lost in the Middle” and long-context challenges: https://ar5iv.labs.arxiv.org/html/2307.03172?ref=cline.ghost.io, https://onnyunhui.medium.com/evaluating-long-context-lengths-in-llms-challenges-and-benchmarks-ef77a220d34d?ref=cline.ghost.io
- Degradation at extreme context lengths: https://arxiv.org/abs/2503.20589?ref=cline.ghost.io, https://www.youtube.com/watch?v=KwRRuiCCdmc&ref=cline.ghost.io
- IBM Research on information overload: https://research.ibm.com/blog/larger-context-window?ref=cline.ghost.io
- On retrieval vs. agency: https://pashpashpash.substack.com/p/why-i-no-longer-recommend-rag-for?ref=cline.ghost.io
- Original announcement: https://cline.bot/blog/focus-attention-isnt-enough
TL;DR
- Cline introduces Focus Chain, an explicit, persistent task plan kept in context to reduce agent drift.
- The feature is paired with a two-stage deep-planning workflow: plan first, then execute with the plan in view.
- The post argues for prioritizing high-value tokens and minimizing broad retrieval that bloats prompts.
- Research links cited note the “Lost in the Middle” effect and quality degradation at extreme context sizes.
- No benchmarks, pricing, or detailed availability beyond the post date are provided.
