Agent Orchestration Tools: A Buyer's Guide for Small Teams

Key takeaways

• Score agent orchestration tools on three criteria: control over what each agent touches, observability into every run, and cost per run in tokens, not per seat.
• Per-seat billing punishes small teams running many agents; shared credit pools split the difference between seat and per-run pricing.
• Treat completion as a review question: acceptance criteria written on the ticket, checked against the diff, mapped back to the ticket.
• Event-driven queueing replaces agents that poll for status, which is where idle token burn happens.
• Test any shortlisted tool on one real cross-repo Linear ticket before committing; if it does not beat pasting prompts yourself, keep the terminals.

The best agent orchestration tools for small teams running parallel coding agents score high on three things: control over what each agent touches, observability into every run, and low cost per run. This guide scores tools on those criteria using real workflows, like queueing agents on Linear tickets and reviewing diffs across repos, and applies them honestly to our own product, Aerunit.

Which agent orchestration tools actually fit parallel coding agents?

Generic agent frameworks schedule tasks. Orchestration tools built for coding agents solve a different problem: keeping a fleet of agents busy on well-scoped work while you stay the reviewer, not the glue between every step.

Three things separate them. First, they integrate with where the work already lives, typically Linear tickets feeding Cursor Cloud Agents or Claude Code sessions. Second, they manage the environment per run, such as git worktrees and branch setup, so parallel agents don't trample each other. Third, they expose what a run cost and where it failed, because "how do I know the implementation is actually complete?" is a review question, not a scheduling one.

Billing models reveal the difference: per-seat tools like Conductor price the human, while credit-pool tools price the agent's compute. We break the full field down in our comparison of cloud coding agents.

How we scored each tool: control, observability, cost per run

Feature checklists hide the tradeoffs that matter when you run six terminals of parallel agents. We scored every tool, including Aerunit, on three questions.

Control: who queues the next prompt

The slow part of your day is rarely the agent. It is the routine around it: pasting the next prompt, checking whether the last one finished, deciding what ships next. Control means the tool decides when work queues, on what triggers, and who can override it mid-run. A tool that only fires tasks on a schedule gives you less control than one that lets you re-order a queue or block a task pending review of the diff.

Observability: can you see where a run went wrong

When an agent mangles a refactor in step one, every queued prompt inherits the mistake. You need a record of which step failed, what context it had, and what the acceptance criteria said. Tools without per-step logs force you to re-run blind.

Cost per run: polling burns tokens

Agents that poll for status burn tokens doing nothing. Score tools on tokens spent per completed task, not per seat. Aerunit is honest here: it does not publish per-run token accounting yet, so you will still need your provider's dashboard alongside it.

The tools compared

The table below scores six agent orchestration tools on the three criteria from the last section: control, observability, and cost per run. Cursor Cloud Agents are the runtime underneath several of these workflows, and their pricing is credit-based per agent run (Cursor documentation).

Tool	Control	Observability	Cost per run
Conductor	Strong: explicit task scoping before agents start	Diff-first review per task	Credits per run, visible in dashboard
Devin (Cognition)	Moderate: session-level, less ticket-shaped	Session replays and logs	Per-seat plus compute credits
Agentastic	Moderate: queue-based, you define the handoffs	Basic run logs	Self-hosted, infra cost
Factory	Strong: fleet-level policies	Diffs and traces per agent	Enterprise pricing, not public
Orgabot	Moderate: Linear-first, ticket-driven	PR-linked status per ticket	Subscription tiers
Aerunit	Strong: acceptance criteria on every ticket	Diff review plus cross-repo awareness	Shared team credit pool

Conductor

Conductor treats each task as a scoped unit an agent picks up, which maps cleanly to a Linear ticket. You review the diff, not the terminal scrollback.

Devin (Cognition)

Devin runs long autonomous sessions with replays for review. The trade-off is less control over how work is decomposed before the session starts.

Agentastic

Agentastic is a queue you feed prompts into, closer to the DIY kanban pattern. You own the handoff logic, which means you also own the failure modes.

Factory

Factory positions itself as fleet management with policy controls. Pricing is not public, so cost per run is hard to compare until you talk to sales.

Orgabot

Orgabot starts from Linear tickets and reports status back per PR. It is narrower than the others, which is either the point or the limit depending on your stack.

Aerunit, scored honestly

Aerunit queues scoped work from Linear into Cursor Cloud Agents and keeps the fleet busy while you review diffs. Its weak spot: observability is diff- and PR-based, so debugging a failed run mid-flight is thinner than Devin's session replays. A deeper scoring breakdown is in our comparison of cloud coding agents.

Running parallel agents on Linear tickets

If your work already lives in Linear, the orchestration layer should read the tickets, not make you re-type them. Aerunit pulls Linear issues as agent tasks, scopes each one with acceptance criteria, and dispatches Cursor Cloud Agents across repos so agents stay busy while you review diffs instead of queueing the next prompt by hand. When a run finishes, the result maps back to the ticket, so "is this actually complete?" gets answered against the criteria you wrote, not vibes.

The honest gap: Aerunit assumes Linear plus Cursor Cloud Agents as the substrate. If your team plans in Jira or runs Claude Code locally, the Linear-specific fit weakens, and that tradeoff is worth naming rather than glossing. Conductor and Factory take broader tool views; we score that in the comparison section.

For a deeper breakdown of how these agentic coding tools differ, see our guide at https://aerunit.ai/de/agentic-ai-coding-tools.

Cross-repo changes and PR review

A change that touches your API repo, the web client, and a shared library breaks the one-repo assumption most agent setups carry. Cursor Cloud Agents can work across a workspace, but an agent only stays aware of the other repos if your orchestration layer feeds it that context, typically by attaching ticket context and linked issues from Linear's API (Linear developer docs). Without that, each agent refactors against a stale picture of the interface it depends on.

The same problem shows up at review time. When three agents open three PRs against the same interface, you need one place to review the diffs and catch the conflict before merge, not after.

Aerunit handles this by scoping work with acceptance criteria and cross-repo awareness, so agents on Linear tickets queue against a shared view of the codebase. The honest gap: Aerunit does not auto-resolve cross-repo merge conflicts; you still review the diff and reconcile manually. Devin and Conductor take similar positions, surfacing PRs for human review rather than merging autonomously.

Open-source and local-first options

Several builders open-sourced their own orchestration layers after hitting the same wall: the agent was fast, the routine around it was not. These projects typically give you a kanban board or pipeline over git worktrees, so agents stay busy while you review diffs instead of queueing the next prompt by hand. They are enough when you are solo, comfortable reading source, and your workflow fits their shape. The trade-offs are real: you own the glue between steps, context degradation across a queued chain is your problem to detect, and there is no team credit pool or Linear integration maintained for you. If a step silently half-completes, you debug it yourself. For a 2-10 person team already paying for Linear and Cursor Cloud Agents, the honest calculus is time spent maintaining your orchestrator versus the cost of one that ships with observability and acceptance-criteria checks built in.

What a run really costs at team scale

Per-seat billing punishes small teams that run six agents from two chairs; per-run billing punishes experimentation, because every throwaway prompt costs money. Credit pools split the difference: the team buys a shared balance, and any agent on any ticket draws from it, so a founder running three Cursor Cloud Agents and an engineer running two pay from one pot.

The catch is observability of spend. If the tool shows cost per run, you can spot the agent that burns tokens polling for status instead of working, which is the failure practitioners describe when they say the routine around the agent was the slow part (I turned my Claude Code routine into a repeatable pipeline). Aerunit bills the team as a credit pool but does not yet publish per-run cost breakdowns, so budgeting means watching your Linear queue, not a dashboard. Ask any vendor for that number before committing.

How to choose in an afternoon

Pick one real ticket from your Linear board, ideally a cross-repo change, and run it through your shortlist. Score each tool on three questions: can you scope the work precisely, can you see what each agent did without reading six terminal logs, and what does one run cost you in tokens and credits?

Then check the failure path. When an agent mangles the first step, does the queue stop, or does it burn tokens on bad context? Does someone still review the diff before merge?

Run the same test on your current setup, even if that setup is you pasting prompts. If the tool doesn't beat the status quo on control and observability at your actual run volume, keep the terminals.

FAQ

Do I need an orchestration tool if I already run six terminals with git worktrees?

Maybe not. If you are solo, comfortable reading source, and your workflow fits a DIY kanban pattern, the terminal setup works. The trade-off is that you own the glue: queueing the next prompt, detecting half-completed steps, and tracking spend. A tool earns its keep when the routine around the agents becomes the slow part of your day.

How do you handle context degradation when one agent's mistake cascades into the next prompt in the queue?

Two defenses: scoping and stopping. Scoping means every queued task carries explicit acceptance criteria, so a bad step produces a diff that fails review rather than looking plausible. Stopping means the queue halts pending your review of a diff instead of feeding downstream prompts on contaminated context.

How do I know an agent's implementation is actually complete before I merge?

Treat it as a review question, not a scheduling one. The answer should come from acceptance criteria you wrote on the ticket, checked against the diff the agent produced, with the result mapped back to the ticket. Reviewing terminal scrollback or trusting a status message is not verification.

What is the best agent orchestration tool for a solo developer chaining Claude Code sessions?

There is no single best; it depends on your substrate. Aerunit assumes Linear plus Cursor Cloud Agents, so it weakens if you run Claude Code locally. Open-source and local-first pipelines suit a solo developer chaining Claude Code sessions, at the cost of maintaining the glue yourself.

Can these tools stop polling from burning tokens while agents wait for review?

Partly. Event-driven queueing replaces agents that poll for status, which is where idle token burn happens. But visibility into spend varies: Conductor shows credits per run in its dashboard, Factory's pricing is not public, and Aerunit bills a shared credit pool without per-run cost breakdowns yet.