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An operator asks Vega, a release assistant, to promote reranker-v17 to 25% traffic. Three checks can run at the same time, but one stale result or result from another tenant must never become a traffic shift.
The request is concrete:
1Promote reranker-v17 to 25% traffic. Cite its eval regressions and tell me whether its canary burned error budget in the last hour.The three checks are independent reads: offline evaluation, canary health, and rollout policy. An eval specialist interprets evaluator notes, a health specialist reconciles telemetry with incident context, and a deterministic node checks policy. The traffic shift waits for every receipt, a human decision bound to one exact proposal, and a final read of live state.
Recursive Language Models kept one controller in charge of an external environment while sub-calls stayed bounded. Agent recovery added retry budgets, checkpoints, and safe fallbacks. Human-in-the-loop then paused Vega before a model promotion. Now the question changes: when branches need different instructions or tools, who owns the shared state they write into?
Trace this request as five stages:
- Triage the request.
- Fan out three read-only checks.
- Merge typed receipts.
- Pause on one approval packet.
- Let one authorized writer recheck state and apply one idempotent effect.
A graph node isn't automatically an agent. Deterministic validators, database reads, and formatters can sit beside model calls without becoming agents themselves. Use an agent only where a step needs language understanding or open-ended tool selection.
What makes this a multi-agent workflow rather than a long prompt?
Answer
Specialized workers have separate instructions or capabilities, and the runtime connects their results through explicit state and edges. Deterministic nodes still own routing, validation, approval, and external effects.
Decide whether multiple agents earn their cost
More agents create extra context windows and parallel work. They also create more model calls, coordination failures, and state to reconcile. Before drawing a graph, ask whether independent work can repay that cost.
Cognition's 2025 warning described parallel agents making conflicting implicit decisions when they couldn't share full traces.[1] Its 2026 follow-up narrowed the useful pattern: several agents can contribute intelligence while writes stay single-threaded.[2] The boundary matters more than the word swarm.
Anthropic studied a different shape in its 2025 Research system: a lead agent delegated independent search directions to parallel subagents. On Anthropic's internal evaluation, a Claude Opus 4 lead with Claude Sonnet 4 subagents outperformed a single Opus 4 agent by 90.2%, while consuming about 15 times as many tokens as ordinary chats.[3] That measurement belongs to one breadth-first research system. It isn't a general speedup or cost multiplier, and the report calls tightly coupled work, including many coding tasks, a poor fit.
| Task signal | Prefer one agent or plain workflow | Consider multiple agents |
|---|---|---|
| Dependency shape | Each step changes what the next step should do | Branches can work from stable, separate inputs |
| Worker type | Reads and rules are deterministic nodes | At least two branches need different instructions or tool capabilities |
| Context | Every worker needs the full trace | Each worker needs a bounded slice plus a shared mission |
| Parallelism | Work is mostly sequential | Independent reads can overlap |
| Merge | Outputs need open-ended reconciliation | A typed merge contract can accept or reject each result |
| Side effects | Reasoning and writes are tightly coupled | Workers propose; one boundary writes |
| Value | Routine and cost-sensitive | High enough to pay for extra calls and review |
Use the signals in the table as an admission gate. Before reading the output, predict the route for three cases: Vega's release checks, deterministic ETL, and a coupled code edit. The function gives a multi-agent graph only when at least two branches need distinct agent instructions or tools, can work from bounded context, have a merge contract, and justify the extra cost. Otherwise it falls back to a deterministic workflow or one scoped agent.
1from dataclasses import dataclass
2
3@dataclass(frozen=True)
4class TaskShape:
5 independent_branches: int
6 agentic_branches: int
7 needs_full_shared_trace: bool
8 merge_contract_defined: bool
9 value_justifies_extra_cost: bool
10
11def choose_orchestration(task: TaskShape) -> str:
12 if (
13 task.independent_branches >= 2
14 and task.agentic_branches >= 2
15 and not task.needs_full_shared_trace
16 and task.merge_contract_defined
17 and task.value_justifies_extra_cost
18 ):
19 return "parallel multi-agent graph"
20 if (
21 task.independent_branches >= 2
22 and not task.needs_full_shared_trace
23 and task.merge_contract_defined
24 ):
25 return "parallel deterministic workflow"
26 return "single scoped agent"
27
28release_checks = TaskShape(3, 2, False, True, True)
29deterministic_etl = TaskShape(3, 0, False, True, True)
30coupled_code_edit = TaskShape(3, 3, True, False, True)
31
32print("release checks:", choose_orchestration(release_checks))
33print("deterministic ETL:", choose_orchestration(deterministic_etl))
34print("coupled code edit:", choose_orchestration(coupled_code_edit))1release checks: parallel multi-agent graph
2deterministic ETL: parallel deterministic workflow
3coupled code edit: single scoped agentPassing the gate doesn't make a system safe. It says only that parallel specialization has a plausible benefit. Vega's promotion request passes; deterministic ETL stays a plain workflow, and the coupled edit stays with one agent. The next design question is control flow: which steps can overlap, and which must wait?
Keep one release workflow stable
A directed acyclic graph (DAG) connects nodes with one-way edges and has no path back to an earlier node. Vega's triage, fan-out, and merge fit that definition while they remain read-only.
Retries and human review change the shape. A retry can return to an earlier step, and a review can pause then resume the run. Once either happens, describe the workflow as a state machine or general graph, even if its read phase is a DAG.
The read path is parallel, but its identity stays singular. Keep these values fixed as the request moves from input to execution:
| Field | Stable value | Owner |
|---|---|---|
tenant_id | tenant-7 | Trusted request boundary |
request_id | promote-104 | Trusted request boundary |
release_id | reranker-v17 | Release-store lookup |
release_version | 3 | Release store, re-read before write |
requested_traffic | 25 | Operator request, later bound into approval |
| Required receipts | eval, health, policy | Three read-only branches |
| Write key | promotion:tenant-7:reranker-v17:25:v3 | Execution boundary |
The state changes in visible stages:
| Stage | New state | What still can't happen |
|---|---|---|
| Input | Tenant, request, release, version, requested traffic | No worker has evidence yet |
| Fan-out | Three branch tasks scheduled | Branches can't approve or write |
| Fan-in | Three source-backed receipts validated | A merge isn't human approval |
| Proposal | Exact action, version, digest, and idempotency key | Traffic remains unchanged |
| Resume | Reviewer returns decision for the same digest and version | Writer must still re-read live state |
| Execute | One idempotent promotion record | Replays reuse the first result |
The trace separates two questions that are easy to mix up. An edge answers "what runs next?" It never answers "who may mutate production?" That second question needs a state contract and an owner, which is where the graph becomes more than a picture of boxes.
Why isn't the full workflow a strict DAG?
Answer
The read path is a DAG, but retries add back-edges and human review adds pause and resume. The shipped workflow is a state machine or general graph that happens to contain an acyclic read phase.
Make state the contract between workers
The three checks become useful only when their outputs meet an explicit contract. LangGraph expresses that contract with state, nodes, and edges: state is the current snapshot, nodes return partial updates, and edges choose what runs next.[4]
The library isn't required to see the mechanism. A tiny superstep runtime can run one barrier of nodes, merge their updates, and start the next barrier. That is enough to make fan-out and fan-in observable.
Now consider two branches writing the same state key. A reducer defines how those concurrent updates combine; without one, the write is ambiguous, and LangGraph raises INVALID_CONCURRENT_GRAPH_UPDATE. Receipt lists can use concatenation because each branch contributes one item. A scalar such as requested_traffic has no sensible concatenation, so one trusted owner must write it. The merge still needs an independent contract: exactly one receipt for each expected branch, with missing, duplicate, and unexpected branches rejected before proposal creation.
The example below makes that barrier visible. On a live release path, eval and health may be bounded agents when their evidence needs semantic interpretation; rollout policy should remain deterministic. Those choices don't change the contract: three nodes run in one barrier, append receipts through a reducer, and wait for merge_receipts.
1import hashlib
2import json
3import operator
4from collections import Counter, defaultdict
5from typing import Callable, NotRequired, TypedDict
6
7class InvalidConcurrentUpdate(ValueError):
8 pass
9
10def merge_updates(
11 state: dict[str, object],
12 updates: list[dict[str, object]],
13 reducers: dict[str, Callable[[object, object], object]],
14) -> dict[str, object]:
15 grouped: dict[str, list[object]] = defaultdict(list)
16 for update in updates:
17 for key, value in update.items():
18 grouped[key].append(value)
19 next_state = dict(state)
20 for key, values in grouped.items():
21 reducer = reducers.get(key)
22 if reducer is not None:
23 merged = next_state.get(key)
24 for value in values:
25 merged = reducer(merged, value)
26 next_state[key] = merged
27 continue
28 if len(values) != 1:
29 raise InvalidConcurrentUpdate(
30 f"{key}: {len(values)} concurrent writes and no reducer"
31 )
32 next_state[key] = values[0]
33 return next_state
34
35def run_superstep(
36 state: dict[str, object],
37 nodes: list[Callable[[dict[str, object]], dict[str, object]]],
38 reducers: dict[str, Callable[[object, object], object]],
39) -> dict[str, object]:
40 return merge_updates(state, [node(state) for node in nodes], reducers)
41
42class Receipt(TypedDict):
43 branch: str
44 tenant_id: str
45 request_id: str
46 ok: bool
47 evidence_ref: str
48 requires_approval: bool
49
50class ReleaseState(TypedDict):
51 tenant_id: str
52 request_id: str
53 release_id: str
54 release_version: int
55 requested_traffic: int
56 receipts: list[Receipt]
57 proposal: NotRequired[dict[str, object]]
58 outcome: NotRequired[str]
59
60REDUCERS: dict[str, Callable[[object, object], object]] = {"receipts": operator.add}
61
62def receipt(
63 state: ReleaseState,
64 branch: str,
65 evidence_ref: str,
66 *,
67 ok: bool = True,
68 tenant_id: str | None = None,
69) -> Receipt:
70 return {
71 "branch": branch,
72 "tenant_id": tenant_id or state["tenant_id"],
73 "request_id": state["request_id"],
74 "ok": ok,
75 "evidence_ref": evidence_ref,
76 "requires_approval": branch == "policy" and state["requested_traffic"] > 10,
77 }
78
79def triage(_: ReleaseState) -> dict[str, object]:
80 return {"outcome": "checking"}
81
82def eval_check(state: ReleaseState) -> dict[str, object]:
83 return {"receipts": [receipt(state, "eval", "evals/run-91")]}
84
85def health_check(state: ReleaseState) -> dict[str, object]:
86 return {"receipts": [receipt(state, "health", "metrics/window-1h")]}
87
88def policy_check(state: ReleaseState) -> dict[str, object]:
89 return {"receipts": [receipt(state, "policy", "policy/rollout-v4")]}
90
91def merge_receipts(state: ReleaseState) -> dict[str, object]:
92 receipts = state["receipts"]
93 expected_branches = frozenset(("eval", "health", "policy"))
94 branches = [item["branch"] for item in receipts]
95 counts = Counter(branches)
96 missing = sorted(expected_branches - set(branches))
97 duplicates = sorted(branch for branch, count in counts.items() if count > 1)
98 unexpected = sorted(set(branches) - expected_branches)
99 contract_errors: list[str] = []
100 if len(receipts) != len(expected_branches):
101 contract_errors.append(
102 f"count={len(receipts)} (expected {len(expected_branches)})"
103 )
104 if missing:
105 contract_errors.append(f"missing={','.join(missing)}")
106 if duplicates:
107 contract_errors.append(f"duplicate={','.join(duplicates)}")
108 if unexpected:
109 contract_errors.append(f"unexpected={','.join(unexpected)}")
110 if contract_errors:
111 raise ValueError("invalid receipt set: " + "; ".join(contract_errors))
112
113 # The contract checks make this map one-to-one: one receipt per branch.
114 receipts_by_branch = {item["branch"]: item for item in receipts}
115 for item in receipts_by_branch.values():
116 if item["tenant_id"] != state["tenant_id"]:
117 raise ValueError("tenant mismatch")
118 if item["request_id"] != state["request_id"]:
119 raise ValueError("request mismatch")
120 if not item["ok"] or not item["evidence_ref"]:
121 raise ValueError(f"invalid {item['branch']} receipt")
122
123 action = {
124 "kind": "promote",
125 "tenant_id": state["tenant_id"],
126 "release_id": state["release_id"],
127 "release_version": state["release_version"],
128 "traffic_percent": state["requested_traffic"],
129 }
130 encoded = json.dumps(action, sort_keys=True, separators=(",", ":")).encode()
131 proposal = {
132 "action": action,
133 "digest": hashlib.sha256(encoded).hexdigest(),
134 "idempotency_key": (
135 f"promotion:{state['tenant_id']}:{state['release_id']}:"
136 f"{state['requested_traffic']}:v{state['release_version']}"
137 ),
138 "evidence_refs": [
139 receipts_by_branch[branch]["evidence_ref"]
140 for branch in sorted(expected_branches)
141 ],
142 }
143 needs_review = any(
144 item["requires_approval"] for item in receipts_by_branch.values()
145 )
146 return {
147 "proposal": proposal,
148 "outcome": "awaiting_approval" if needs_review else "ready_for_writer",
149 }
150
151try:
152 merge_updates(
153 {"requested_traffic": 25},
154 [{"requested_traffic": 10}, {"requested_traffic": 25}],
155 reducers={},
156 )
157except InvalidConcurrentUpdate as exc:
158 print("unreduced scalar:", exc)
159
160initial: ReleaseState = {
161 "tenant_id": "tenant-7",
162 "request_id": "promote-104",
163 "release_id": "reranker-v17",
164 "release_version": 3,
165 "requested_traffic": 25,
166 "receipts": [],
167}
168state = run_superstep(initial, [triage], REDUCERS)
169state = run_superstep(state, [eval_check, health_check, policy_check], REDUCERS)
170state = run_superstep(state, [merge_receipts], REDUCERS)
171summary = [
172 f"{item['branch']}:{'pass' if item['ok'] else 'fail'}"
173 for item in state["receipts"]
174]
175print("route: triage -> [eval_check, health_check, policy_check] -> merge_receipts")
176print("receipts:", ", ".join(sorted(summary)))
177print("outcome:", state["outcome"])
178print("write executed:", False)1unreduced scalar: requested_traffic: 2 concurrent writes and no reducer
2route: triage -> [eval_check, health_check, policy_check] -> merge_receipts
3receipts: eval:pass, health:pass, policy:pass
4outcome: awaiting_approval
5write executed: Falseoperator.add preserves each receipt, but it doesn't validate them. merge_receipts now checks exact count and one-to-one branch coverage before checking tenant and request scope, status, and evidence references. A set comparison alone would accept eval, health, policy, policy; the Counter plus count check rejects that duplicate. The reducer answers "how do concurrent updates combine?" The validator answers "can this combined value be trusted?"
Four receipts arrive: eval, health, policy, and a second policy. Why must the merge reject them even though the set of branch names looks complete?
Answer
Set membership hides duplicates. The merge requires exactly three receipts and uses per-branch counts, so it reports count=4 and duplicate=policy before building a proposal.
LangGraph's waiting-edge API makes the same barrier explicit. add_edge(["eval_check", "health_check", "policy_check"], "merge_receipts") runs the merge after all three named branches finish. With branches at different depths, three separate add_edge calls can schedule the merge more than once. The wiring below reuses the lab's node functions; it isn't a second runnable script.
1import operator
2from typing import Annotated, TypedDict
3from langgraph.graph import END, START, StateGraph
4
5class GraphState(TypedDict):
6 receipts: Annotated[list[Receipt], operator.add]
7 # remaining fields omitted
8
9builder = StateGraph(GraphState)
10builder.add_node("triage", triage)
11builder.add_node("eval_check", eval_check)
12builder.add_node("health_check", health_check)
13builder.add_node("policy_check", policy_check)
14builder.add_node("merge_receipts", merge_receipts)
15builder.add_edge(START, "triage")
16builder.add_edge("triage", "eval_check")
17builder.add_edge("triage", "health_check")
18builder.add_edge("triage", "policy_check")
19builder.add_edge(["eval_check", "health_check", "policy_check"], "merge_receipts")
20builder.add_edge("merge_receipts", END)Types describe shape, not authority. TypedDict helps editors, type checkers, and readers understand the schema, but a compromised node can still return fields it shouldn't own. The trusted runtime must enforce write ownership:
| Node | May write | Must never write |
|---|---|---|
triage | Requested branch set or recorded route | Approval, release version, traffic result |
| Read branch | Its own receipt | Another branch's receipt, proposal, approval |
merge_receipts | Validated proposal and outcome | Human identity or completed side effect |
| Approval node | Review decision bound to proposal | Replacement proposal |
| Writer | Execution receipt | New policy facts or a different action |
Returning only changed fields makes ownership review easier. Returning the whole state from every node invites accidental overwrites and hides which worker produced each value.
Why does the receipt list need both a reducer and a validator?
Answer
The reducer tells the runtime how concurrent list updates combine. The validator decides whether the combined receipts are complete, scoped to this request and tenant, source-backed, and safe to consume.
Persist, retry, and resume by semantics
The read DAG ends when it creates an approval packet, not when it changes traffic. Human-in-the-loop established the pause: persist the exact action, bind the review to its digest, and re-read live state before any write.
A production graph stores Vega's thread, waits, and resumes that same thread after review. LangGraph checkpointers save graph state at superstep boundaries and use thread_id to identify the thread to load.[5]
The packet carries four pieces of identity: an action digest, release version, evidence references, and idempotency key. The digest hashes the exact action fields the reviewer saw. Change traffic from 25% to 50%, or release version from 3 to 4, and the packet is different, so it needs a new review. Rechecking that identity at execution prevents a time-of-check-to-time-of-use (TOCTOU) failure across the pause.
LangGraph's current approval primitive is interrupt(), backed by a checkpointer. The caller resumes with Command(resume=...) and the same thread_id.[6] Static interrupt_before and interrupt_after breakpoints still exist, but current documentation recommends interrupt() for human-in-the-loop workflows.
The approval node below shows the boundary. It returns a routing Command; it doesn't execute the promotion itself.
1from typing import Literal
2from langgraph.types import Command, interrupt
3
4def approval_node(
5 state: ReleaseState,
6) -> Command[Literal["execute", "cancel"]]:
7 proposal = state["proposal"]
8 decision = interrupt({
9 "question": "Approve this promotion?",
10 "proposal": proposal,
11 })
12 approved = (
13 decision.get("approved") is True
14 and decision.get("digest") == proposal["digest"]
15 and decision.get("release_version")
16 == proposal["action"]["release_version"]
17 )
18 return Command(goto="execute" if approved else "cancel")On resume, LangGraph restarts the interrupted node from its beginning. Code before interrupt() can run again.[6] Keep side effects after the pause or make them idempotent.
Retries need their own boundary. A retry policy answers "should this node attempt the same operation again?" A graph edge answers "should the workflow change strategy?" Mixing those questions creates loops that look resilient while repeating a bad decision.
| Failure | Correct response | Reason |
|---|---|---|
| Metrics read times out | Bounded retry with backoff | Operation is read-only and failure may be transient |
| Release ID isn't found | Route to alias lookup or human review | Same request will keep returning not found |
| Receipt has wrong tenant | Fail merge immediately | Retry can't repair a trust-boundary violation |
| Reviewer rejects proposal | Route to cancel | Rejection is a decision, not a transient error |
| Release version changes during review | Build a new packet and review again | Old approval no longer names current state |
| Writer times out after sending | Retry with same idempotency key | Outcome may be unknown, so deduplicate at storage boundary |
The final writer models the timeout case. Replaying the same key with the exact action returns the first promotion record. Reusing that key for different arguments is rejected, rather than treating a different traffic shift as the already-approved operation.
1PromotionAction = tuple[str, int, int]
2promotion_ledger: dict[str, tuple[str, PromotionAction]] = {}
3LIVE_RELEASE_VERSION = {"reranker-v17": 3}
4
5def execute_promotion(
6 release_id: str,
7 approved_version: int,
8 traffic_percent: int,
9 idempotency_key: str,
10) -> str:
11 if LIVE_RELEASE_VERSION[release_id] != approved_version:
12 return "blocked:stale_release"
13 action = (release_id, approved_version, traffic_percent)
14 if idempotency_key in promotion_ledger:
15 promotion_id, original_action = promotion_ledger[idempotency_key]
16 if original_action != action:
17 return "blocked:idempotency_key_mismatch"
18 return f"reused:{promotion_id}"
19 promotion_id = f"promotion-{len(promotion_ledger) + 1}"
20 promotion_ledger[idempotency_key] = (promotion_id, action)
21 return f"created:{promotion_id}:{traffic_percent}%"
22
23key = "promotion:tenant-7:reranker-v17:25:v3"
24print("first:", execute_promotion("reranker-v17", 3, 25, key))
25print("replay:", execute_promotion("reranker-v17", 3, 25, key))
26print("changed action:", execute_promotion("reranker-v17", 3, 50, key))
27print("stale:", execute_promotion("reranker-v17", 2, 25, "stale-key"))
28print("ledger records:", len(promotion_ledger))1first: created:promotion-1:25%
2replay: reused:promotion-1
3changed action: blocked:idempotency_key_mismatch
4stale: blocked:stale_release
5ledger records: 1InMemorySaver is suitable for local examples, not process restarts. A workflow that must survive a crash or wait for review needs a durable checkpointer such as Postgres. The current Postgres integration uses PostgresSaver.from_conn_string(...), requires its checkpoint tables to be set up, and compiles the graph with that saver.[5]
A writer times out after sending a promotion request. Should the graph create a new idempotency key for its retry?
Answer
No. The timeout leaves the outcome unknown. Retry the same approved action with the same key so the deployment boundary returns the first result instead of applying a second shift.
Choose topology from ownership
Vega's release workflow is map-reduce: split independent checks, then validate one merged result. That ownership contract won't fit every task. Some jobs need a manager to choose workers; others need one specialist to take over the conversation.
Name the contract before naming the framework. Cognition's 2026 follow-up makes a similar cut: unstructured agent swarms stay a distraction, while the practical shape is map-reduce-and-manage with single-threaded writes.[2]
| Pattern | Who owns the run? | Best fit | Main failure |
|---|---|---|---|
| Map-reduce | Runtime owns fan-out; reducer owns fan-in | Independent searches, evals, or checks | Missing, duplicated, or incompatible branch results |
| Supervisor | Manager agent retains control | Worker choice isn't known in advance | Extra model calls and a routing bottleneck |
| Handoff | Receiving agent takes over | Triage into one specialist conversation | Lost context, capability leakage, or circular transfer |
| Hierarchy | Root delegates to local managers | Large domains with real sub-team boundaries | Intent dilution and high routing latency |
AutoGen's original work explored multi-agent conversation as an application pattern.[7] Current AgentChat offers SelectorGroupChat, where a model chooses the next speaker, and GraphFlow, where a directed graph controls sequential, parallel, conditional, and looping execution.[8] GraphFlow is still documented as experimental. Pin its version and test serialized workflow behavior before relying on it.
OpenAI's Agents SDK supports two different ownership choices. A handoff is exposed to the model as a transfer tool and gives the receiving agent control of the conversation. An input filter can change what history the receiver sees.[9] An agent used as a tool returns a bounded result while the manager keeps control.[10]
The handoff wiring is small, but it doesn't carry authority with it. The application boundary still owns authentication, tool permissions, turn limits, and external writes.
1from agents import Agent
2
3release_agent = Agent(
4 name="Release Agent",
5 handoff_description="Handles release status and rollout-plan questions.",
6 instructions="Explain release evidence. Never execute a traffic shift.",
7)
8health_agent = Agent(
9 name="Health Agent",
10 handoff_description="Handles canary metrics and error-budget questions.",
11 instructions="Explain current health evidence from approved tools.",
12)
13triage_agent = Agent(
14 name="Triage Agent",
15 instructions="Transfer this conversation to one matching specialist.",
16 handoffs=[release_agent, health_agent],
17)Handoff history is untrusted context. Filtering a transcript can remove irrelevant messages, but it doesn't authorize the receiver or let it inherit the sender's tools. Rebuild the receiver's capability set from trusted application configuration.
When should a specialist be a tool instead of a handoff?
Answer
Use a specialist as a tool when one manager should retain conversation ownership and consume a bounded result. Use a handoff when the specialist should take over the conversation.
Separate workflow frameworks from wire protocols
LangGraph, AutoGen, and the OpenAI Agents SDK orchestrate code inside one application. The Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) describe boundaries between components that may be developed and deployed separately. The distinction is ownership, not whether a model appears in the path.
MCP lets a host discover and invoke server-provided tools and read resources or prompts.[11] Its 2026-07-28 revision made the protocol core stateless and moved long-running Tasks into an extension.[12] A stateful application can still return an explicit handle from one tool call and require that handle on later calls.
A2A 1.0 models communication with independent remote agents. A client can send a message, receive a stateful Task for longer work, follow status updates through polling, streaming, or webhooks, and consume outputs as Artifacts.[13] Messages carry communication; Artifacts carry task outputs.
Long-running work no longer separates the protocols by itself because both can represent it. Choose from the boundary you need:
| Need | MCP | A2A |
|---|---|---|
| Expose a database lookup or deployment API to a model host | Strong fit as a tool | Usually unnecessary |
| Read a resource or reusable prompt | Native primitive | Not its main abstraction |
| Run a long tool call | Tasks extension can represent it | Task lifecycle can represent it |
| Delegate to an independent specialist agent | Possible behind a tool, but agent identity is flattened | Native agent and task boundary |
| Return a durable task output | Structured tool result or resource | Artifact attached to Task |
| Grant production authority | Never implied by protocol | Never implied by protocol |
For Vega's release workflow, MCP can expose get_eval_receipt, get_health_receipt, and get_rollout_policy to workers. A2A fits only if one worker is an independent remote agent whose task identity, progress, and artifact lifecycle matter to the caller. Neither protocol replaces tenant checks, approval binding, or writer authorization.
Can MCP now represent long-running work, and does that make it the same as A2A?
Answer
MCP's Tasks extension can represent long-running tool execution. A2A still models an independent agent and its task, message, status, and artifact lifecycle. Choose based on counterparty and ownership, not duration alone.
Diagnose failures from state and ownership
The graph now gives each failure somewhere to land. Diagnose a symptom by its state transition, owner, evidence, and repair instead of replying with a generic "retry the agent."
| Symptom | Likely cause | Evidence to inspect | Fix |
|---|---|---|---|
INVALID_CONCURRENT_GRAPH_UPDATE | Parallel nodes wrote one unreduced key | State schema and node updates in same superstep | Add a valid reducer or assign one writer |
| Merge runs with incomplete evidence | Fan-in edge or dynamic branch contract is wrong | Scheduled branches and receipt set | Use an explicit join and require exact branch identities |
| Two traffic shifts appear | Writer replayed without storage-level deduplication | Idempotency ledger and request key | Retry same key and enforce uniqueness at service boundary |
| Approved action differs from executed action | Approval stored as a Boolean | Displayed digest, source version, live proposal | Bind review to exact action and version, then re-read live state |
| Agents hand off forever | No turn or recursion limit | Handoff trace and repeated owners | Add hard turn limits and a forced failure or review edge |
| Supervisor spends more than workers | Model routes every small step | Per-node tokens and latency | Move stable routes onto deterministic edges |
| Worker sees another tenant's fact | Receipt or reference lost scope | Tenant and request fields at fan-in | Reject cross-scope data before merge |
| Receiver gains a write tool after handoff | Capabilities were inherited from transcript or sender | Runtime tool inventory at transfer | Rebuild tools from trusted receiver policy |
| State grows on every turn | Full records or transcripts are copied forward | Checkpoint size and per-node context | Store large data by reference and pass verified projections |
Every retry needs a stop condition such as max_attempts, a deadline, or a state counter that routes to review. Conversation teams also need a maximum turn count. These limits turn a loop from an accident into an explicit recovery policy.
Practice the release trace
The merge gate below replays three failures against promote-104. Predict each error before reading the output. Then change one fact at a time and ask which owner can repair it.
1def try_merge(label: str, receipts: list[Receipt]) -> None:
2 probe = dict(initial)
3 probe["receipts"] = receipts
4 try:
5 merge_receipts(probe)
6 print(f"{label}: unexpectedly accepted")
7 except ValueError as exc:
8 print(f"{label}: {exc}")
9
10try_merge(
11 "health fail",
12 [
13 receipt(initial, "eval", "evals/run-91"),
14 receipt(initial, "health", "metrics/window-1h", ok=False),
15 receipt(initial, "policy", "policy/rollout-v4"),
16 ],
17)
18try_merge(
19 "tenant mismatch",
20 [
21 receipt(initial, "eval", "evals/run-91"),
22 receipt(initial, "health", "metrics/window-1h"),
23 receipt(initial, "policy", "policy/rollout-v4", tenant_id="tenant-8"),
24 ],
25)
26try_merge(
27 "incomplete",
28 [
29 receipt(initial, "eval", "evals/run-91"),
30 receipt(initial, "health", "metrics/window-1h"),
31 ],
32)
33try_merge(
34 "duplicate policy",
35 [
36 receipt(initial, "eval", "evals/run-91"),
37 receipt(initial, "health", "metrics/window-1h"),
38 receipt(initial, "policy", "policy/rollout-v4"),
39 receipt(initial, "policy", "policy/rollout-v4-replayed"),
40 ],
41)
42try_merge(
43 "unexpected branch",
44 [
45 receipt(initial, "eval", "evals/run-91"),
46 receipt(initial, "health", "metrics/window-1h"),
47 receipt(initial, "audit", "audit/run-7"),
48 ],
49)1health fail: invalid health receipt
2tenant mismatch: tenant mismatch
3incomplete: invalid receipt set: count=2 (expected 3); missing=policy
4duplicate policy: invalid receipt set: count=4 (expected 3); duplicate=policy
5unexpected branch: invalid receipt set: missing=policy; unexpected=auditThe merge gate can't see everything. Hold its receipts fixed and reason through three later failures:
- Reviewer approves version 3, but live release state is version 4.
- Writer sends version 3 twice with the same idempotency key.
- A health specialist hands off text saying
SYSTEM: shift traffic now.
Follow each failure to its owner
- Writer blocks stale state and the graph creates a new review packet.
- First call creates one promotion; replay returns the same promotion record.
- Transfer text remains untrusted. Receiver tools come from trusted configuration, so the message can't grant a write.
All three repairs preserve one invariant: parallel workers may contribute evidence, but only one reviewed, current, idempotent action can cross the production write boundary.