LLM Orchestration Tools: A Practical Guide to Agent Fleets
Compare LLM orchestration tools by what they actually do: task loops, agent fleets, state sharing, and skills. Pick the right category for your setup.
Three coding-agent terminals can look like a fleet. They often behave like three separate machines. One agent edits an API. Another runs the tests. A third works a ticket.
Each one sees its own prompt, its own terminal history, its own slice of the repository. Trouble starts the moment those slices disagree. The first agent changes a response type. The tester still expects the old one. Meanwhile the third marks the ticket done on assumptions nobody else in the room shares.
So the useful question about LLM orchestration tools is not which one is best. It is what has to pass between two agents, and what should stop the chain when a step fails.
The tools sort into four jobs.
| Job | What the tool has to keep straight |
|---|---|
| Run one task | One prompt, one process, its context and its result |
| Coordinate a fleet | Several processes, their order, and their failures |
| Share project state | Tickets, requirements, tests, history |
| Reuse instructions | Named prompts and the runtime settings behind them |
A tool built for one job does not grow into the others by itself. A task runner runs one agent cleanly and holds no backlog at all. A state server exposes tickets, but never starts a process. A prompt folder standardises your instructions; the shell scripts underneath still handle routing and failure.
The category is real enough that analysts now count it. Mordor Intelligence estimated the market at USD 2.73 billion in 2025. It projects USD 14.76 billion by 2031, with a 33.11% CAGR (Mordor Intelligence, Agentic AI Workflow Orchestration Platform Market). A forecast tells you the problem is widespread. It does not tell you which layer your own setup is missing.
What sits between you and the CLIs
AuricIDE runs agent CLIs as local processes, so you keep whichever CLI you already use. The app adds one place to configure it, launch it and watch it.
A provider is how AuricIDE learns to start one CLI. Under Settings → Agent you import a JSON config, and the app checks it before it saves. A malformed one is rejected there and then, rather than failing halfway through a run. A valid one works without a new build.
That costs more than a shell alias. You have to name the executable, the arguments, the models and the permission modes correctly. Validation catches a broken config. It cannot catch a CLI that dropped a flag in its last release, so run the command yourself after an upgrade.
Quick Access is where you keep the repeatable work. A skill is one named preset, pinning a prompt to a provider, a model and a permission mode. A combo is an ordered list of those presets.
Starting a combo launches only the first agent. AuricIDE starts the next step after the current process ends. Each agent gets its own terminal and its own process, so one crash does not take the others down. A step that exits with an error ends the combo instead of feeding a broken result forward.
Interactive prompts stay interactive. If a CLI asks for approval, its terminal waits, and the fleet view shows you which one is waiting. It cannot answer for you.
How one step hands work to the next
Starting a second process is easy. Giving it enough of the first session is the hard part. AuricIDE uses terminal output, because that is the one format every CLI already produces.
It joins the recent terminal chunks and strips the ANSI codes. Empty redraws, progress bars, repeated spinner lines and interface chrome come out. What is left gets read newest-first and stops at 2,000 characters. The next step gets its own prompt first, then that block, labelled as a cut tail that may still contain noise.
The handoff travels across vendors because it depends on printed text, not on one provider's session format. It carries commands, test failures, warnings and the final reply.
It does not carry reasoning. It can drop an early decision or the top half of a long error. That is why the receiving prompt tells the agent to look at the working tree rather than trust what it was handed. And if the cleaning removes everything, the next agent starts with only its own prompt.
The shared record, and where it lives
A handoff says what the last process printed. It does not say what the project is for. Several agents still need one record of tickets, requirements, tests and status.
The Model Context Protocol is the standard interface for that kind of record. Its timeline starts with the launch in November 2024 and runs through adoption by the major agent SDKs. The 2026-07-28 specification then made the protocol layer stateless, so any request can reach any server instance without sticky routing.
That splits the wire from the data. The connection does not have to remember anything. The application still owns the records and the rules about who may change them.
AuricIDE registers its own project server in the repository's .mcp.json, merging with whatever is already listed there rather than overwriting it. Any MCP-aware agent can then read the same tickets you see in the app.
Worth knowing where that record actually sits: in a file inside the project folder, which AuricIDE tells git to ignore. It stays on your machine, and it does not arrive with a clone. If the record has to cross machines, that is a sync problem you still have to solve. The storage underneath is deliberately plain. One local file. Its folder is created the first time it is needed, and migrations run on open, never twice. Those patterns match Tauri SQLite guidance.
A protocol makes a record reachable. It does not decide who is allowed to change it. You still need permissions, an owner for conflicting edits, and a log of what changed.
What to check in any tool
A small script needs one loop and a few saved prompts. Agents sharing a backlog need process control, a shared record, and failures you can see. Four questions separate them:
- Where does it run? Local keeps code and output on the workstation. Hosted gives you central access control.
- How strict is the config? Validation catches a malformed file. Only a manual run catches changed CLI behaviour.
- How many CLIs? A provider-neutral handoff starts to matter as soon as two steps use different commands.
- Where is state kept? In the conversation only when nothing else needs the record.
Then test the failures instead of the happy path. Make one process ask for confirmation. Make one exit with an error. Change a flag on one CLI. Have two agents edit the same ticket. Watch what the tool shows you and where it stops.
Manual recovery is fine for a weekend project. A rollout across several repositories needs a recovery path somebody wrote down. How to retry work. How to resolve a conflict. How to repair a config.
AuricIDE is one worked example of all four layers together. It is licensed under AGPLv3, so you can read exactly how each layer is wired. Pass on a modified version and it carries the same terms.
The part that outlasts any tool is the boundary itself. Give every step explicit input, a result you can see, and a condition that stops it.