Guide, keyword: what is multi agent orchestration
Multi agent orchestration is four files on your main branch, not a vendor definition.
Every vendor page you read today defines multi agent orchestration so their product is the answer. AWS defines it around Bedrock Agents. Talkdesk defines it around Talkdesk. Microsoft defines it around AutoGen. None of those definitions survive the vendor leaving. Ours does. We define orchestration by what has to be committed to your main branch on the last day of the engagement: one orchestrator file, one eval harness, one failure playbook, one runbook.
Defined by what is on main at handoff. Not by whose framework you bought.
graph.py / eval.yml / FAILURE_PLAYBOOK.md / RUNBOOK.md
The dictionary definition, and why it is not enough
The dictionary definition of multi agent orchestration says: a process by which multiple specialized agents coordinate, share context, and collaborate toward a shared goal, usually with a dedicated orchestrator routing work among them. That definition is correct. It is also compatible with a prototype that falls over the first time a model provider degrades, a pipeline with no audit trail, and a demo whose runbook lives in someone’s head.
The dictionary will not help you at 2am. The on-call engineer picking up a failed transition does not care that agents coordinate. They care about four questions: which node failed, where is its audit row, which model handled the request, and what is the safe rollback. The orchestration that can answer those four questions is the orchestration you want. Everything else is a seminar.
“Files on main at week 6 define whether the orchestration is real: graph.py, eval.yml, FAILURE_PLAYBOOK.md, RUNBOOK.md. If any one of them is missing or stale, you have a demo that happens to be in production.”
PIAS week 6 handoff checklist, applied across five shipped systems
The operational definition, as an inputs / orchestrator / outputs diagram
Inputs arrive at the orchestrator. The orchestrator routes, checks, falls back, and logs. The outputs are not just the response to the user; they are also the four artifacts that make the orchestration legible to the next engineer. This is the shape we commit to main every time.
Multi agent orchestration, by what walks out of the engagement
The leave-behind layer: what you can grep for after we leave
The four leave-behind artifacts, named
Each card is one file (or one workflow) that must be merged to main before we sign the handoff. We do not ship orchestration that has three of the four. Three of four is a prototype. Four of four is a production system with a definition.
1. graph.py or pipeline.py
One file that defines the orchestration: nodes, edges, conditional fallback, entrypoint. LangGraph StateGraph, a Pydantic AI pipeline, or a custom scene DAG. Your engineers read this file first on every incident.
2. Eval harness in CI
A ragas-based rubric plus a case-specific test suite wired into GitHub Actions. Runs on every pull request. Flags regressions before they hit production. No human reviews prompts by hand.
3. Failure playbook
Names the primary model, the fallback model, the SLO breach that triggers the swap, the audit-log schema, and the Postgres table the regulator queries. A single markdown file, checked in next to graph.py.
4. On-call runbook
Keyed to your actual on-call rotation. Answers the three questions a pager answers at 2am: which node failed, where is its audit row, what is the safe rollback. Ends with the escalation path.
What handoff actually looks like in git log
This is the shape of commits on main when an engagement ends. Not every commit, but the ones that ship the four artifacts. After the handoff call, we ask the on-call to run git log --oneline src/agents/ and confirm each file is there and each workflow is green. If any of them is missing, we are not done.
Artifact 1: the orchestrator, in one readable file
This is the shape we commit to main on Upstate Remedial’s compliance email flow. One typed state object. One entry point. One conditional edge from the primary model to the fallback on SLO breach. One audit hook that fires after every transition and writes exactly one row to Postgres. A regulator reading the audit table can reconstruct any message from the row set.
Artifact 2: the eval harness, in your CI
The rubric is not in a dashboard we host. It is in your repo, in a GitHub Actions workflow, on every pull request that touches the orchestrator. If the rubric score falls below the gate (we use 0.82 by default; you can pick a different number), the PR cannot merge. That is the entire enforcement mechanism. No vendor login, no separate product.
What multi agent orchestration is not
A lot of things shipped as orchestration in 2024 and 2025 are not orchestration under the handoff definition. That is fine as a prototype. It is not fine as a production system with regulatory exposure or real revenue attached. If any of these look like what your team has, the honest answer is that you have a prototype with orchestration branding.
Six things often miscalled multi agent orchestration
- A framework you install once and call orchestration done
- A vendor dashboard that watches agents you cannot export
- A swarm of autonomous agents with no named failure domain
- A prompt chain dressed up in orchestration vocabulary
- A diagram with arrows but no audit rows
- Anything that cannot answer which node failed at 2am
Vendor definition vs handoff definition
Both sides of the table are coherent. The left column is how AWS, Talkdesk, Kore.ai, IBM, and most vendor blogs define multi agent orchestration. The right column is how we define it, because we do not sell a platform and we have to leave the system in a state your engineers can run without us.
| Feature | Typical vendor definition | Handoff definition (PIAS) |
|---|---|---|
| Primary unit of definition | A coordination pattern (centralized, distributed, adaptive) | A set of files on main that outlive the engagement |
| What survives when the vendor leaves | Usually a seat license and a hosted runtime | The orchestrator, the evals, the runbook. No hosted runtime. |
| How you answer a 2am incident | Open a ticket with the vendor | Open graph.py and the audit-log row |
| Where the rubric lives | In a vendor dashboard | In your GitHub Actions, on every PR |
| Who can replace the engineer who built it | The vendor's support team | Any senior engineer who can read Python |
| Swap the model provider | Re-negotiate the contract | Edit one adapter file, re-run evals |
| Commercial lock-in | Platform license on the MSA | Model-vendor neutral by contract |
The week 2 prototype gate, the week 6 production rubric
The definition is not just four files; it is the schedule that produces them. Week 2 is a prototype gate your board can hold us to. Week 6 is the production rubric. If week 2 slips, we tell you on day 10. If week 6 cannot be met with the four artifacts, the engagement does not end.
Week 0: scoping
Sixty-minute call. We name the failure domain, draw the state shape, and write the one-pager that defines the target orchestration and the acceptance rubric. No code yet.
Week 2: prototype gate
A working orchestrator on a branch, hitting the happy path end to end. The eval harness exists with at least one scored rubric. If this gate slips, we tell you on day 10, not day 40.
Weeks 3 to 5: hardening
Conditional fallback edges wired. Audit-log rows on every transition. Canary rollout path for model changes. Your engineers pair with us at least two days a week so the code is not foreign when we leave.
Week 6: production rubric
The orchestrator is live. The four leave-behind artifacts are merged to main. A 90-minute handoff session walks your on-call through a staged incident. Same engineer stays on retainer for 12 months of capped 2-hour consults.
Receipts
These are not sector averages; they are counts from PIAS engagements. The five shipped systems are named on /wins. The six-week cadence is the one we publish publicly and commit to in every scoping call.
The 0 systems include 0K+ emails on the LangGraph flow at Upstate Remedial and 0K/day on the Pydantic AI pipeline at Monetizy.ai. Every one of those systems shipped the four leave-behind artifacts to main at handoff.
Anchor fact
Three framework choices across five shipped systems. Four identical leave-behind artifacts on every one.
On Monetizy.ai the orchestrator file is a Pydantic AI pipeline because the problem is a scored pipeline at ~8K messages per day. On Upstate Remedial it is a LangGraph StateGraph because the problem is one typed conversation per case with a regulatory audit row per transition. On OpenArt it is a custom scene-graph DAG because the state is a tree of sub-scenes with per-scene retry. The framework choice is downstream of the shape. The four leave-behind artifacts are constant across all three, because the definition of orchestration is constant across all three.
If your current AI vendor ships the same framework for all three problem shapes, or cannot commit to the four artifacts on main at week 6, you are paying for orchestration branding. Not orchestration.
Want the handoff definition applied to your repo?
Sixty-minute scoping call. You leave with a one-pager naming the orchestrator file, the eval rubric, the failure playbook shape, and the runbook roles. Same engineer, week 0 to week 6, plus 12 months on retainer.
Book a call →Multi agent orchestration: the questions we actually get on scoping calls
What is multi agent orchestration, in one sentence?
Multi agent orchestration is the set of files on your main branch that defines how specialized agents are routed, how they fall back when one fails, how every transition is logged, and how a new engineer reads the graph at 2am. If you cannot point at those files after the engagement ends, you do not have orchestration. You have a demo.
Why not just use the dictionary definition (agents coordinate to solve a task)?
Because the dictionary definition is compatible with five products that cannot survive a handoff. AWS, Talkdesk, Kore.ai, and the OpenAI Agents SDK each define orchestration so their product is the answer. We do not sell a product, so we define orchestration by what must exist on your repo when we walk out. That is a stricter test, and it is the test that matters when the vendor leaves.
What are the four leave-behind artifacts, exactly?
One, the orchestrator definition file (graph.py for LangGraph, pipeline.py for Pydantic AI or a custom DAG). Two, an eval harness: ragas plus a case-specific rubric wired into GitHub Actions, blocking merge on regression. Three, FAILURE_PLAYBOOK.md: the primary model, the fallback model, the SLO breach that triggers the swap, the audit-log schema. Four, RUNBOOK.md: keyed to your on-call rotation, covering which node failed, where the audit row is, and how to roll back safely. Four files. Merged to main. Reviewed with the on-call team in a 90-minute handoff session.
Is this a framework? Do we have to use LangGraph?
No. We have shipped LangGraph, Pydantic AI, and custom DAGs depending on the state shape of the problem. The definition is framework neutral by design. What the definition requires is that whichever framework you use, its orchestration definition lives in one readable file, its evals run in your CI, and a named engineer can be paged at 2am without calling the vendor.
What is the anchor fact that makes this definition uncopyable?
Same engineer, same consultancy, three different framework choices across five shipped systems. Monetizy.ai runs a Pydantic AI pipeline because the orchestration is a scored pipeline at ~8K emails per day. Upstate Remedial runs a LangGraph state machine because each email is a single typed conversation with per-transition audit rows; 400K+ emails sent. OpenArt runs a custom scene-graph DAG because its state is a tree of sub-scenes with per-scene retry. All three shipped the same four leave-behind artifacts. If you can point at those four files a month later, the orchestration is real.
What does week 6 look like if our team has never shipped a production agent?
We pair-program at least two days a week from week 1. Your engineers are on every PR, on every eval run, and on the handoff call. The runbook names two of your engineers by role, not ours. At week 6 we rehearse an incident: we page the on-call, walk them through the audit query, and verify the rollback. If your team cannot answer the three runbook questions unassisted, we do not sign the handoff.
How does this differ from a typical AI agency or LLM consultancy?
Most agencies leave behind a prototype and a Notion doc. We leave behind four files on main plus a 12-month capped retainer so the same senior engineer is reachable when you hit an edge case. No platform license, no vendor-attached runtime, no hidden handoff fee. Model-vendor neutral by contract. You keep the keys, you keep the bill, and you can replace us with any senior engineer who reads Python.
What if the orchestration we need is a new shape not covered by LangGraph or Pydantic AI?
We write a custom DAG. OpenArt's pipeline is thirty lines of Python that walk a scene tree post-order, and it is the simplest honest model of that problem. The definition of orchestration does not depend on any framework existing. It depends on the file being readable, the evals running in CI, and the runbook being current.
Can you production-harden an existing LangGraph or CrewAI prototype we already have?
That is how most engagements start. Week 0 is a read of your graph, the nodes with no fallback, no eval signal, or no audit row. Week 2 is the hardened prototype. Week 6 is the four leave-behind artifacts on main. We do not rewrite working code; we add the things that make it survive handoff.
Where can I see the case studies with the orchestration choices called out?
/wins lists the five shipped systems with the framework choice, the stack, and the outcome metrics. /t/multi-agent-orchestration reverse-engineers the framework selection across those five systems into a repeatable decision matrix. This page tells you what orchestration has to look like on your repo regardless of which framework you pick.
Adjacent guides
More on shipping production multi agent systems
Multi agent orchestration: the framework selection matrix
Five shipped systems, three framework choices. LangGraph, Pydantic AI, and custom DAGs. The decision matrix reverse-engineered from real engagements.
The six-week FDE engagement model
Week 0 scoping, week 2 prototype gate, week 6 handoff. The cadence and the artifacts your board can hold us to.
Shipped systems, cited on the record
Monetizy.ai, Upstate Remedial, OpenLaw, PriceFox, OpenArt. Named clients, production metrics, per-system stacks.