How a platform team runs a fleet of AI agents without the drift: shared versioned mission context over MCP, handoffs that carry state, an append-only trace of every run, optimistic-concurrency conflict detection, and per-run cost that reconciles into per-mission spend.
A platform team running more than one or two agents hits the same wall regardless of vendor: each agent keeps its own private context, work forks, handoffs happen in Slack, and nothing holds the record of what the fleet actually did. The fix is a coordination plane above the agents — one versioned source of truth per mission that every agent reads before it acts and writes through with a version check, handoffs that carry state, and an append-only trace that every agent reports into. Quordo provides that plane over MCP and A2A, so agents you already built on any framework join without bespoke glue, and their per-run cost reconciles into per-mission spend.
Who this is for: Platform engineering, developer-experience, and AI-infrastructure teams running coding agents and multi-agent workflows in production.
Key takeaways
Today's agents scale one person's output but not the alignment between them; the coordination between agents is still done by humans in Slack and standups.
The unit that needs a shared source of truth is the mission, not the agent — one versioned context every agent reads before acting and writes through with an expected version.
Optimistic concurrency is what makes many agents on one mission safe: a losing write returns a conflict the agent reconciles, instead of silently overwriting another agent's work.
A coordination plane is the only layer that can compute cost per mission, because it is the only one that knows which run belonged to which piece of work.
The questions this role asks
How do I connect Claude Code, Cursor, or Devin to a coordination layer?
Register each agent once to issue it a per-agent report-in key, then add one MCP server entry to that agent's config with the key and the coordination API URL. The agent discovers the coordination tools automatically and can list missions, read and write shared context, bracket work in runs, and create handoffs. Register one agent per logical worker rather than sharing a key, or the trace loses the attribution that makes it worth reading.
Because each one holds its own context window as the source of truth. Two agents given the same task start from the same brief, then make independent decisions about interfaces, naming, and assumptions that neither records anywhere the other can read. By the time the work merges, the divergence is in the code rather than in a document. Shared, versioned context external to any agent's window is the structural fix — the agents read the same facts instead of each keeping a private copy.
Is this orchestration or coordination — do I still need my agent framework?
You still need it. Orchestration is control flow inside one application: a framework decides which step runs next and passes state between them. Coordination is the layer above several independently-operating agents that may be built on different frameworks and run on different machines — it gives them shared truth, handoffs, and a record, without deciding what any of them does next. A framework orchestrates the agents you build; a coordination plane governs the agents you have already built.
How do I know whether a multi-agent mission is going well?
Measure agreement, not activity. Coherence rolls up how much of a mission's shared context the participating agents actually agree on — unresolved write conflicts, stale reads, and contradictory facts drag it down. It is a leading indicator: coherence falls before the work visibly breaks, which is the window in which a human intervention is still cheap. Counting runs or messages measures effort and tells you nothing about alignment.
What does a piece of multi-agent work actually cost?
Only the coordination layer can answer that. Provider billing is organized around keys and accounts, but a key is shared across many missions and a mission spans many keys, so neither view slices back to the work. If every agent run reports its cost and carries its parent mission, cost per mission is a sum — and it is a figure no provider invoice can produce.
Every mission has a versioned key-value context that any MCP agent reads before acting and writes through with an expected version. Claude Code, Cursor, Devin, Claude Desktop, and custom agents all join with one config entry and a per-agent key.
Optimistic-concurrency conflict detection
A context write that loses a version race returns a conflict error rather than silently overwriting. The agent re-reads and reconciles, and unresolved conflicts are exactly what the mission's coherence percentage counts.
Handoffs that carry state
An agent hands work to another with a reason and payload attached, so the next agent continues from the mission's context instead of rediscovering it. A handoff left unaccepted past its SLA escalates and flags the mission for a human.
An append-only trace of everything the fleet did
Runs, context reads and writes, handoffs, messages, conflicts, and pauses land in one ordered mission trace across every connected agent — with per-step latency and errors on the run waterfall.
A2A alongside MCP
Quordo publishes an agent card and speaks Google's A2A JSON-RPC as well as MCP, so agents on either protocol join the same missions without bespoke glue.
Per-run cost that reconciles into per-mission spend
Runs report their cost when they close, and that cost flows into the same plane that attributes provider spend by team — so 'what did this mission cost across every agent' has one answer, not a spreadsheet.
A layer above a fleet of independently-operating AI agents that gives them a shared, versioned source of truth per mission, a way to hand work off with state attached, and an append-only record of everything they did. It does not decide what any agent does next — that stays with the agent or its framework — it makes several agents accountable to one truth and one trace.
Which agents can join — do they have to be built on one framework?
No. Any agent that speaks MCP joins by adding one server entry to its config with a per-agent key, and agents that speak Google's A2A protocol join over the A2A JSON-RPC endpoint. That covers Claude Code, Cursor, Devin, Claude Desktop, and custom agents on any framework — the coordination plane sits above your frameworks rather than replacing them.
How does Quordo prevent two agents from overwriting each other?
Context writes carry an expected version. If another agent changed that key first, the write is rejected with a conflict error instead of applied, and the agent re-reads and reconciles. That is standard optimistic concurrency, and it turns a silent lost-update into an explicit, resolvable event that a human can be pulled into.
How is this different from an agent framework like LangGraph or CrewAI?
Those are frameworks for building one multi-agent application — they own control flow inside it. Quordo is the cross-vendor plane above whatever you built: shared truth, coordination, governance, and audit at the organization level, across agents from different frameworks and vendors. It complements a framework rather than competing with it.
Does coordinating agents through Quordo add latency to a run?
Coordination calls are ordinary API calls the agent makes at its own boundaries — read context at the start, write when a decision is made, close the run at the end — not a proxy in the model path. Prompts and completions never route through Quordo, so inference latency is unchanged.