<- all agent manager tools

ai agent orchestration tools for coding

Swarm, DAG, dispatch, and team-agent tools for breaking coding work into parallel agent tasks.

where orchestration sits

Orchestration is the rung above agent management. Where a manager runs the sessions you decide to spawn, an orchestrator decides which sessions exist in the first place.

the orchestration ladder — least → most autonomous
note each rung trades human judgment for throughput. that trade-off is the whole question.

what orchestrators actually add

decomposition. Take one prompt ("rebuild the auth flow") and split it into N sub-tasks appropriate to parallel work.

dispatch. Spawn agents — locally via worktrees, or remotely on cloud workers — and assign sub-tasks.

supervision. Watch for stuck agents, retry, escalate, or kill. Some define this as a DAG with dependencies between sub-tasks.

merging. Take the parallel diffs and stitch them into one branch / PR. This is the part that breaks most often.

local vs cloud — the real fork

local orchestratorcloud orchestrator
runs onyour machinevendor infrastructure
isolationworktreescontainers / VMs per agent
review unitdiff in your editorPR or deploy preview
cost modelyour existing subscriptionsper-task or per-hour billing
typical scope2-8 sub-tasks per promptend-to-end feature

when orchestration is the right reach

- large fan-out work — one task that splits cleanly into many parallel pieces (e.g. migrate 30 files)

- unattended runs — overnight batches you do not want to babysit one terminal at a time

- DAGs with real dependencies — task B genuinely needs task A's output before it can start

- team policy — central place to apply rate limits, model pinning, audit logs

when orchestration is the wrong reach

Orchestration multiplies your throughput and your review surface area in lockstep. Four agents working in parallel produce four diffs you have to read. If your bottleneck is review (it usually is) then orchestration moves the bottleneck rather than removing it.

The classic mistake: reach for swarms before the local prompt-to-diff-to-merge loop works. More agents do not fix a broken loop, they just generate more diffs to abandon.

decision criteria

- does it dispatch work or run the local workspace?

- does it keep humans in the review loop?

- does it require a server, cloud, or special runtime?

- does it produce reviewable worktrees or opaque output?

pane vs this category: the short version

Pane is the local cockpit where terminal agents run and get reviewed. Orchestrators can be complementary if they launch CLI agents.

tools in this category

toolconstraintwhat pane does instead
AstroDAG dispatch across hosts. fire-and-forget.complementary; Pane is where the dispatched agents run locally
AI Team OSClaude Code only. heavy templates and dashboards.lighter local cockpit for any CLI agent
DeepStevebrowser UI. self-hosted PTY server.native desktop app, no server
AgentsMeshremote AgentPods over gRPC. infra-heavy.local-first, no provisioning required
Shardauto-dispatches subtasks; reduces human review.keeps the human in the review loop on every diff

common mistake

Reaching for swarm orchestration before the local review loop works. More agents produce more diffs, and those diffs still need ownership.

Want the local cockpit before adding orchestration on top? Download Pane.

useful next pages

- all agent manager tools

- Pane vs autonomous agents

- Pane vs CLI agents

frequently asked questions