With AI coding agents assuming more responsibility inside development tools, they're moving beyond single-prompt interactions to ongoing, multi-step processes. This comes with its own set of challenges — keeping context intact and maintaining continuity as work evolves. However, these challenges compound when teams start running multiple agents in tandem: chats multiply, follow-ups overlap, and handoffs become harder to track, making it difficult to see how everything fits together. Amp, the AI coding agent recently spun out of Sourcegraph, is tackling that problem head-on with Thread Map, a new way to visualize how threads (e.g. individual chats or lines of work with an agent) relate to each other over time. So rather than treating conversations as isolated logs, the thread map shows how different threads are linked — which ones stem from earlier discussions, which ones reference prior work, and which ones continue it. This makes it easier to understand how a piece of work developed over time, rather than having to piece it together from separate conversations. recently spun out of Sourcegraph tackling that problem head-on What a Thread Map looks like Thread Map is only available through the Amp CLI for now, where running threads: map in the command palette opens the visual view of connected threads. threads: map Crucially, this isn’t just a static overview. From the CLI map view, users can select a thread, press Enter, and jump straight into it, continuing their work from there. Enter Amp presents Thread Map as nodes in a graph, showing relationships created through references, continuations, or handoffs. The goal is to make activity across related conversations easier to understand — particularly as work starts to branch and connect in more complex ways. The bigger picture: Keeping context in check Underneath that visual layer, Thread Map is very much about keeping context under control. Amp describes common patterns where one central thread holds the core context, while related threads branch off to handle refactors, sub-features, or follow-on work without carrying unnecessary baggage. In other cases, work progresses through a chain of short threads, each passing along only the context that’s needed to continue. This keeps conversations focused and lightweight, even as work becomes more interconnected over time. Alongside Thread Map, Amp is adding Thread Labels, which let users tag and filter threads by topic or purpose. Combined with the map, they make it easier to scan complex workflows and focus on what matters. Amp is adding While look_at isn’t explicitly tied to threads, it reflects the same underlying concern: keeping context manageable as inputs grow. By isolating file analysis from the main flow, it helps prevent long-running conversations from becoming bloated or unfocused. look_at Making agents easier to manage Collectively, these updates highlight a growing shift away from what agents can do isolation, toward how their output can be tracked, understood, and refined over time. Thread Map adds visibility into how work connects, labels add lightweight structure, and look_at helps keep external material from overwhelming the broader flow. look_at None of this changes how agents reason or remember internally. More, it addresses a practical bottleneck: helping people make sense of increasingly connected conversations, handoffs, and follow-on work. As agent-use moves beyond single prompts into longer-running tasks, that kind of clarity will certainly matter.

Too Many Agent Chats? Amp’s New Thread Map Shows What Connects to What

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