cool project. one thing most fleet tools miss: session persistence and replay matter more than the dashboard. when an agent goes sideways at 2am you want to scrub back through exactly what it did, not stare at a pretty graph. also cross-agent context -- knowing agent B is blocked waiting on agent A's output -- thats where coordination actually breaks down. curious how you handle that handoff visibility?

On session persistence & replay:

  Honest answer: full scrub-back replay isn't built yet, but the foundation is deliberate. Terminal output flows through a relay ring buffer (256KB / 200 messages) — enough for a late-joining browser to     
  catch up mid-session. The runner side keeps a styled virtual terminal with scrollback history per pod. What's not there yet is durable server-side recording — backend explicitly doesn't persist terminal   
  bytes. What is there is agent session resume: when a pod goes sideways, you can spin up a new pod that inherits the same git worktree and agent conversation context (Claude Code --session-id), so the agent
   picks up from where it left off rather than starting cold. The replay gap is real and on the roadmap.                                                                                                       

  On cross-agent handoff visibility:

  This is where the implementation is actually solid. Three things work together:

  1. Agent status detection — the runner analyzes PTY output in real-time with a multi-signal detector and emits executing / waiting / idle states, which are written to the pod DB and pushed to the frontend.
   So you literally see agent B sitting in waiting state on the topology view.
  2. Pod bindings — the permission layer that makes handoffs explicit. Agent A requests a binding to agent B with scopes (terminal:read, terminal:write). Binding status (pending → active → inactive) is
  persisted in Postgres, so the edge between nodes in the topology graph has state. A pending binding that hasn't been accepted is visible — that's your "B is waiting on A" signal.
  3. Mesh topology API — computes a live graph of all pods (nodes) and their bindings (edges), including what scopes are granted vs. pending. The frontend renders this in real-time so you can see the
  coordination graph without digging through logs.

  Channel messages between agents are also DB-persisted (unlike terminal output), so the coordination dialogue survives pod restarts. The gap is that terminal output isn't currently durable beyond the
  session — that's the honest tradeoff we made in favor of real-time streaming performance. Replay is the next thing to close.

the binding state as deadlock signal is clever. terminal durabilty is the obvious next shoe — glad you know it.

thank you for your feedback!

you cut off mid-sentence but honestly same energy as every 2am incident im already sweating

The handoff visibility problem jlongo78 describes is the core issue. We ran into it building a 9-agent team and solved it differently: instead of modeling cross-agent dependencies as pod bindings at the infrastructure layer, we model them at the task layer.

When agent A completes a task, it posts to a shared task board and transitions state to 'validating'. Agent B polls for tasks in validating state that match its domain. The dependency is in the task state machine, not the agent infrastructure. Deadlocks become visible because the task sits in validating with no owner claim.

The tradeoff: this works when you can model your work as discrete tasks with clean state transitions. It breaks down for continuous / streaming work where tasks don't have clear completion signals.

We open-sourced this as reflectt-node (Apache 2.0). Complementary approach — AgentsMesh handles the infrastructure/session layer, reflectt-node handles the coordination protocol layer above it.