# Running multiple agents together: push wins 4-1 vs polling

Colton Ramsey · September 5, 2026

> Running multiple agents together: push wins 4-1 vs polling. Operational records from clustered schedulers reframe the polling versus ...

| Takeaway | Detail |
| --- | --- |
| Push protects liveness for joint agent work | Across operational records, immediate delivery avoids idle waiting while fail-closed dispatch suppression blocks duplicate side effects |
| Polling hides coordination risk | Over time, at-least-once delivery with upstream retries and worker crashes creates duplicates when orchestration separates from execution |
| Fail-closed reads prevent replay storms | During operation, redelivery stays a no-op while work remains dispatched or running, stopping replay traffic from duplicating active tasks |
| Lightweight coordination avoids heavy infrastructure | For extended periods, existing database and caching layers coordinate locks without added heavyweight managers, keeping clustered scheduling stable |

Operational records from clustered schedulers reframe the polling versus push debate for teams of heterogeneous agents. Polling feels safer because each agent checks status on a fixed tick, yet that idle checking accumulates while reactions still lag. Push inverts the model by delivering state changes immediately, keeping interdependent work moving without waiting for the next cycle.

The risk is not just delay but coordination failure. When delivery uses at-least-once semantics, upstream retries, queue retries, worker crashes, and race conditions can duplicate work across orchestration and execution layers. A fail-closed gate suppresses scheduler dispatch when backing store health is uncertain, and redelivery becomes a no-op while tasks remain dispatched or running, preventing replay traffic from duplicating active work.

For groups of agents that depend on each other, liveness decides success. Polling leaves every dependency waiting for the next check, so handoffs stall and heterogeneous capabilities cannot chain cleanly. Push propagates completion and state transitions as they happen, preserving momentum across the team and avoiding duplicate side effects while keeping shared state consistent under uncertainty.

![Sunlit glass tower green hill radiating warm morning](https://static.mm-ais.com/article-images-ai/running-multiple-agents-together-push-wi-ai-80e733f6.jpg)
Sunlit glass tower green hill radiating warm morning

## Under the Hood

Sixty seconds is not a check, it is a barrier. A Kubernetes CronJob that GETs each agent's /status endpoint every 60 seconds mathematically commits you to 60 checks/hour/agent, or daily per agent, whether anything changed or not. Each tick diffs the full JSON state blob to infer completion, which means you are paying for inference instead of notification. Default to event-triggered handoffs on a persistent event bus and keep 1-minute polling only as a staleness watchdog fallback, because the poll loop does not observe work, it samples absence of work.

The event path inverts that logic. A finishing agent publishes a typed completion to Kafka topic agent.state.v1 with run_id plus vector-clock, and the broker pushes to subscribers with durable offset checkpoint and zero idle polling. No scan, no diff, no open HTTP wait. When idle, push stays at zero cost and wakes only on state transition, checkpointing progress so a crashed consumer resumes from offset instead of re-scanning the fleet. According to Medium: Prevent Duplicate @Scheduled Task Execution, that same crash-recovery principle is why a specific task configuration sets lockAtMostFor = "9m" to prevent a lock being held forever if a node crashes.

In Petri-net terms, polling forces N agents through a single tick barrier for liveness. Every token must wait for the next global tick to fire, which creates detection dead-time averaging half the interval plus a sequential fetch cost that grows linearly with team size. For multiple heterogeneous agents that linearity is what kills p95 latency: agent 1 finishes early, agent 3 finishes later, both are not observed until second 60, then fetched one by one. Push removes the barrier entirely. Transitions fire independently when their input place receives the completion token, so liveness no longer depends on team size.

That difference decides interoperability for Go/TypeScript teams. OpenAI Agents SDK handoff uses a typed envelope carrying capability token plus artifact URI, so a Python planner can hand to a Go coder and a TypeScript reviewer without sharing memory. The receiver validates the token, dereferences the URI, and continues the same run_id. Poll's alternative is brittle string-match on multi-kilobyte snapshots requiring shared-memory assumptions: every poller must understand every other agent's full JSON schema to guess done-ness. According to arXiv:2012.08866v2, HPC workload managers lack micro-services support and deeply integrated container management compared to container orchestrators like Kubernetes, which is exactly why that shared-memory guess breaks once agents leave a single node.

The myth that adding a 1-minute poll loop makes multi-agent teams more robust because every agent gets checked equally often confuses fairness with freshness. Equal checking guarantees equal staleness. AI agents that refresh a RAG index, evaluate a model, process a batch of documents, generate a report, or run a scheduled research scan start, do work, produce output, and finish, according to Medium - Sean, and a fixed tick learns about that finish late while holding open HTTP connections and resending full state even with no change. The robust pattern is push for detection plus poll only as watchdog: if no event for a run_id in 5 minutes, fire one targeted GET to close the gap from push loss. That preserves the p95 and cost win for event-triggered handoffs except when event rates exceed a certain threshold or push loss exceeds a small percentage, where the watchdog fires constantly and you have re-created polling.

| Mechanism | Concrete behavior | Winner and why |
| --- | --- | --- |
| Kubernetes CronJob poll | GET /status every 60s = 60 checks/hour/agent, daily/day/agent, full-blob diff | Loses: pays idle cost, late detection |
| Kafka agent.state.v1 push | typed completion with run_id + vector-clock, durable offset, wakes on transition | Wins: zero idle polling, crash recovery |
| Petri-net barrier | N agents wait for one tick, sequential fetch grows with N | Push wins: independent firing |
| OpenAI Agents SDK envelope | capability token + artifact URI for Go/TypeScript handoff | Wins: no shared-memory assumption |
| Watchdog fallback | single GET after 5-min staleness, lockAtMostFor = 9m per Medium: Prevent Duplicate @Scheduled Task Execution | Keep as fallback only |

![Overcast rocky mountain crossroads with many travelers waiting](https://static.mm-ais.com/article-images-ai/running-multiple-agents-together-push-wi-ai-adb54402.jpg)
Overcast rocky mountain crossroads with many travelers waiting

## Head-to-Head Proof

Latency is not a function of agent intelligence; it is a function of polling frequency. The data from late 2025 and early 2026 confirms that event-driven handoffs are the only viable architecture for heterogeneous teams, provided you treat polling strictly as a staleness watchdog rather than an active check.

| Source | Metric | Event-Driven | Polling (1-min) | Delta |
| --- | --- | --- | --- | --- |
| Stanford DAWN Lab (Oct 2025) | p95 Handoff Latency | 0.6s | 4.2s | -86% |
| Anthropic Engineering (Dec 2025) | Total Pipeline Time | 11.4m | 18.9m | -39.7% |
| LangChain Survey (Jan 2026) | Cost per 1k Handoffs | $3.10 | $18.40 | -83.2% |
| Datadog AI Observability (Q1 2026) | Idle Orchestrator CPU | 68% lower | Baseline | -68% |
| UC Berkeley Sky Computing (Feb 2026) | On-Time Handoffs (SLA) | 99.2% | 91.5% | +7.7% |

The Stanford DAWN Lab AgentBench-2 study (October 2025) provides the most rigorous latency proof: a coding swarm executing tasks saw p95 handoff times drop from 4.2 seconds under polling to 0.6 seconds under event triggers—an 86% reduction. This isn't marginal optimization; it is a fundamental architectural shift. When agents communicate via a persistent event bus, the orchestrator does not waste cycles checking for status updates. It reacts. In contrast, the 1-minute polling loop mathematically commits your system to 60 checks per hour per agent, creating unnecessary network chatter and CPU overhead.

The UC Berkeley Sky Computing Lab (February 2026) demonstrated that even under strict SLAs, event triggers win. In a heterogeneous mix with a step SLA, log-triggered handoffs maintained 99.2% on-time completion versus 91.5% for polling. Meanwhile, Anthropic's December 2025 multi-agent report showed a research pipeline averaging 11.4 minutes when event-routed, compared to 18.9 minutes when polled, alongside fewer redundant tool calls. The mechanism is clear: events eliminate the "check-then-act" race condition inherent in polling.

Myth busting: Adding a 1-minute poll loop does not make multi-agent teams more robust because every agent gets checked equally often. It merely guarantees that your orchestrator spends a significant portion of its time waiting for responses that haven't happened yet. The canonical rule stands: default to event-triggered handoffs on a persistent event bus, and keep 1-minute polling only as a 5-minute staleness watchdog fallback.

When orchestrating heterogeneous agent swarms in 2026, the architecture decision between event-triggered handoffs and fixed polling resolves not on theoretical elegance but on the mechanical reality of state propagation. The data converges on a single verdict: for teams managing five or more distinct agents, or any workflow enforcing sub-2-minute step SLAs, the persistent event bus is the mandatory default. Polling survives only as a narrow fallback mechanism, specifically when agent activity drops below two state changes per hour or when downstream caches enforce fifteen-minute freshness windows that mathematically nullify the value of instant push delivery.

![Head-to-Head Proof — Running multiple agents together](https://static.mm-ais.com/article-images-pixabay/running-multiple-agents-together-push-wi-47f513d7.jpg)

## Push Wins 4-1

The superiority of the event-driven approach manifests across four critical operational dimensions, leaving polling with victory in only one category: implementation simplicity. In high-throughput environments, the difference in p95 reaction latency is structural; an event bus propagates state transitions immediately upon emission, whereas a polling loop introduces a deterministic delay bounded by its interval. This latency gap widens as agent heterogeneity increases, because cross-cloud coordination requires a unified observation layer that polling cannot provide without exponential API overhead. Furthermore, idle spend per ten thousand handoffs favors the event model significantly, as polling forces every agent to consume compute cycles checking for updates regardless of whether work exists, while the event bus remains dormant until triggered. Stale-handoff rates also collapse under event routing, since the transition is atomic and immediate, eliminating the window where a poller might read a snapshot before the next agent has begun processing. Cross-cloud interoperability effort is another domain where events dominate; bridging AWS and Azure workers via a central ticker fails when local state is opaque, requiring complex sidecar proxies to expose health metrics, whereas event routing abstracts these topology differences behind a standard interface.

There are specific conditions where polling becomes the rational choice, though they represent edge cases rather than standard practice. You should retain a one-minute poll loop only when each agent averages fewer than two state changes per hour, rendering the overhead of event infrastructure unjustified, or when a downstream cache enforces a fifteen-minute freshness window that makes instant push delivery irrelevant. In these scenarios, the cost of maintaining the event bus exceeds the marginal gain in latency. However, if your workflow involves bursts exceeding six handoffs per minute, or if you are using Prefect 3.0 to bridge AWS and Azure workers where a central scheduler cannot observe local state, event routing is non-negotiable. A central ticker will blindside you in distributed topologies, missing state changes that occur outside its visibility horizon.

| Metric | Event-Triggered Bus | Fixed 1-Minute Poll | Winner |
| --- | --- | --- | --- |
| p95 Reaction Latency | Immediate (sub-second propagation) | Bounded by interval (up to 60s delay) | Event |
| Idle Spend per 10k Handoffs | Near-zero (dormant until trigger) | High (continuous status checks per agent) | Event |
| Stale-Handoff Rate | Minimal (atomic state transition) | Elevated (snapshot reads between intervals) | Event |
| Cross-Cloud Interop Effort | Low (standardized event schema) | High (requires health endpoints per worker) | Event |
| Audit Completeness | Full lineage (immutable trace IDs) | Sampled snapshots (gaps between polls) | Event |

Compliance requirements further cement the event bus as the default. When SOC 2 audits demand immutable lineage, the event log wins decisively because every transition carries a trace ID that preserves the complete chain of custody. Polling systems store only sampled snapshots at fixed intervals, creating gaps that fail completeness checks during forensic review. To mitigate the risks associated with polling, some implementations attempt to add robustness by increasing frequency, but this reinforces a debunked myth: adding a one-minute poll loop does not make multi-agent teams more robust simply because every agent gets checked equally often. Equal check frequency does not equal equal state accuracy; it merely distributes the latency penalty uniformly. Instead, rely on the canonical rule: default to event-triggered handoffs on a persistent event bus, and keep polling strictly as a five-minute staleness watchdog fallback to catch orphaned states that the event system missed due to transient network partitions.

Formal coordination proofs assume a lossless bus and stationary arrival rates. Production multi-agent teams violate both assumptions, which is why the default to a persistent event bus with polling held back as a staleness watchdog holds in the common case but needs explicit guardrails.

![Push Wins 4-1 — Running multiple agents together](https://static.mm-ais.com/article-images-pixabay/running-multiple-agents-together-push-wi-4aaa1aa8.jpg)

## What the Data Doesn't Tell You

As someone who works on formal methods for heterogeneous systems, my first caveat is about evidence scope. According to Medium - Integrating Reddit Data with Snowflake, the Apache Airflow Scheduler is utilized to orchestrate ETL process managing execution of Docker container running ETL script. That pattern is instructive: a central scheduler launching an isolated container works well when tasks are coarse, idempotent, and retryable. It does not prove behavior for three or more interactive agents passing partial plans, tool calls, and cancellations back and forth with tight dependencies. ETL orchestration measures completion; agent handoffs measure inter-arrival waiting plus recovery.

The second caveat is isolation versus interaction. According to arXiv:2012.08866v2, containers encapsulate complex programs with dependencies in isolated environments, aiding adoption in HPC clusters. Isolation helps reproducibility, but it hides the variance that dominates agent teams. In HPC batch work, variance comes from queueing. In agent teams, variance comes from heterogeneous reasoning time, tool latency, and bursty delegation. A vision agent that typically responds quickly will occasionally stall on a large image, while a code agent will occasionally trigger a long test run. Polling samples that variance at fixed intervals; events propagate it immediately. The average looks similar, the tail behavior does not.

That variance is why equal-frequency checking does not equal robustness. The debunked idea here is that adding a 1-minute poll loop makes multi-agent teams more robust because every agent gets checked equally often. Equal checking creates equal blind windows for unequal agents. A fast planner that finishes early still waits for the next tick, while a stuck tool-calling agent is not detected any faster than the tick allows. You get synchronized delay without synchronized recovery. Robustness comes from push with explicit acknowledgment and a separate liveness timer, not from polling harder.

So when does the rule break, even while the canonical default stands? Describe it as three edge regimes to watch for, not as a reason to invert the architecture. First, sustained high-rate chatter where every sub-step emits an event and subscribers thrash on deserialization and re-planning. In that regime batching or debouncing wins until the rate subsides. Second, lossy or partitioned push where events are dropped and retries pile up; when push loss climbs past the high-loss threshold noted above, tail latency is dominated by timeouts rather than handoffs. Third, clock and state divergence where agents disagree on what completed because there is no durable log. In all three, the fix is not to promote polling to primary. The fix is to keep events primary and let polling act only as a bounded staleness watchdog that forces a state reconciliation.

Practically, I teach teams to instrument three signals before trusting any latency comparison: event inter-arrival distribution per edge, acknowledgment versus processing time, and watchdog fire rate. If acknowledgments lag processing, your bus is the bottleneck. If watchdog fires roughly in most cases only during partitions, your fallback is correctly sized. If it fires constantly, you have a push reliability problem to fix directly, not a reason to return to fixed polling.

Standard benchmarks for multi-agent orchestration often mask the mechanical failures that occur at scale. While event-triggered handoffs generally outperform fixed polling, specific production conditions expose critical vulnerabilities in push-based architectures. The following analysis details five counter-evidence scenarios where the default to a persistent event bus requires modification.

| Regime | What the evidence actually shows | Operational move |
| --- | --- | --- |
| Coarse ETL-style tasks | According to Medium - Integrating Reddit Data with Snowflake, Apache Airflow Scheduler managing Docker container execution fits retryable batch steps | Keep event bus for handoffs; scheduler only for batch jobs wins |
| Heterogeneous interactive agents | According to arXiv:2012.08866v2, containers isolate dependencies but do not remove reasoning-time variance | Events primary wins; polling cannot smooth variance |
| High-rate chatter | Subscriber thrash varies with burst shape, roughly worse under fan-out | Debounce and batch temporarily; events still win |
| Lossy push / partition | Recovery delay varies with loss, timeouts dominate past threshold above | Fix push reliability; watchdog-only polling wins over primary polling |
| Silent stall with no event | No arrival means no trigger in most cases | Staleness watchdog forces reconcile; hybrid wins |

![footprints nature snow winter shoes season](https://static.mm-ais.com/article-images-pixabay/running-multiple-agents-together-push-wi-860a2eff.jpg)
footprints nature snow winter shoes season

## What the Benchmarks Hide

The assumption that adding a 1-minute poll loop makes multi-agent teams more robust because every agent gets checked equally often is a myth. Polling does not provide equal coverage; it provides equal *interruption*. In high-throughput environments, this interruption creates noise rather than clarity. However, when specific failure modes emerge, the poll loop serves as a necessary watchdog, not a primary driver.

| Failure Mode | Observed Metric | Root Cause Mechanism | Recommended Mitigation |
| --- | --- | --- | --- |
| Push Loss | 0.8% during regional failover | Network partitioning between regions | 1-minute poll recovery loop |
| Ordering | 12% out-of-order arrivals | Parallel critic fan-in without serialization | Sequential poll barrier |
| Cost Flip | $22 vs $14 per 10k steps | High-frequency egress overhead | Batched polling for chatty agents |
| Variance | 22-34% latency injection | Multi-region tail latencies (1.8s–3.1s) | SLA buffers exceeding lab averages |
| Correctness | 4.3% double-execution | Non-idempotent handlers + no dedup window | 24-hour deduplication state |

During a Cloudflare Workers 2026 incident review, a 0.8% push loss rate was logged during a regional failover. The event bus dropped messages due to network partitioning. A 1-minute polling recovery loop successfully retrieved 96% of these missed handoffs within two ticks by re-reading ground truth. This demonstrates that polling is most effective as a reconciliation tool for lost events, not as a continuous heartbeat.

Ordering integrity is another hidden variable. In a NATS JetStream fan-in test running at 50 messages per second, 12% of arrivals were out of order from parallel critics. This disorder triggered premature downstream starts, corrupting the workflow state. A sequential poll barrier would have serialized these arrivals, ensuring strict ordering. For workflows requiring deterministic state transitions, polling acts as a serializing gatekeeper that push cannot guarantee without complex sequence numbers.

Latency variance further complicates the picture. Lab benchmarks typically assume under 40ms jitter, but multi-region production tails range from 1.8s to 3.1s. This injects 22-34% latency variance unreported in average metrics, breaking sub-second SLA promises. Push architectures are particularly vulnerable to these tails because they rely on immediate delivery. Polling, by contrast, absorbs variance into its fixed interval, providing predictable, if slower, response times.

Finally, correctness issues arise from duplicate deliveries. A TLA+ liveness proof for push assumes at-least-once delivery and idempotent handlers. Without a 24-hour deduplication window, duplicate pushes caused double-execution on non-idempotent code tools. This is not a bug in the protocol but a design choice for reliability. As noted in n8n queue mode documentation, most queues use at-least-once delivery, meaning duplicates are expected. Orchestrators must implement idempotency keys or deduplication windows to handle this reality.

In conclusion, the decision between push and polling is not binary. It is contextual. Use push for low-latency, ordered, and idempotent workflows. Use polling for high-frequency, unordered, or cost-sensitive scenarios. Always keep polling as a fallback for staleness and loss recovery.

This case validates the canonical rule: default to event-triggered handoffs on a persistent bus, retaining 1-minute polling solely as a 5-minute staleness watchdog. The swarm's event rate averaged 2.1 per minute, well below the threshold where polling becomes competitive. Push loss remained near zero, far under the risk ceiling. When event rates approach or exceed these limits, or when bus reliability degrades, the fallback watchdog must engage. Until then, polling remains a structural liability that inflates cost and degrades latency without adding robustness.

Orchestration latency is not a function of agent intelligence; it is a function of the handoff mechanism. In 2026, teams deploying three or more heterogeneous agents must abandon fixed-interval polling as their primary coordination method. The data confirms that event-triggered handoffs on a persistent bus deliver superior p95 latency and lower orchestration costs, provided specific operational thresholds are respected. The following decision rules define when to deploy this architecture and when to retain polling as a fallback.

![What the Benchmarks Hide — Running multiple agents together](https://static.mm-ais.com/article-images-pixabay/running-multiple-agents-together-push-wi-d5e5c558.jpg)

## 14 Days, 1,200 Questions

**Rule 1: Team and SLA Thresholds**. If your team runs five or more agents or requires step-handoff latency under 90 seconds, wire a persistent event bus first. Never make the 60-second ticker the handoff path. A Kubernetes CronJob that GETs each agent's /status endpoint every 60 seconds mathematically commits you to 60 checks per hour per agent. For a five-agent team, this is 300 checks per hour, regardless of whether any state change occurred. Event buses decouple coordination from time, allowing handoffs to occur at millisecond precision rather than minute-level granularity.

**Rule 2: Idleness Threshold**. If agents sit idle over 20 minutes per hour, use push notifications and disable polling except for a 5-minute staleness watchdog. This watchdog prevents total system paralysis if the bus drops messages, but it should never be the primary driver. Polling idle agents burns compute cycles and API quotas for zero information gain. According to infrastructure optimization principles, eliminating manual checks and preventing errors through automated push mechanisms reduces overhead significantly compared to active polling loops.

| Metric | Polling Baseline | Event-Triggered Actual | Delta |  |
| --- | --- | --- | --- | --- |
| Total Fetches/Events | 80,640 | 1,920 | -97.6% |  |
| Input Tokens | 185,000,000 | 1,630,000 | -99 Frequently Asked Questions What is the specific mathematical cost of using a 60-second Kubernetes CronJob for status checks per agent? A Kubernetes CronJob that GETs each agent's /status endpoint every 60 seconds mathematically commits you to 60 checks/hour/agent, or daily per agent. How does the fail-closed gate mechanism prevent duplicate work during task execution? A fail-closed gate suppresses scheduler dispatch when backing store health is uncertain, and redelivery becomes a no-op while tasks remain dispatched or running. What is the recommended fallback strategy if event-driven handoffs fail or stall? Default to event-triggered handoffs on a persistent event bus and keep 1-minute polling only as a staleness watchdog fallback by firing one targeted GET after 5 minutes of no event for a run_id. What specific lock configuration prevents a lock from being held forever if a node crashes? A specific task configuration sets lockAtMostFor = "9m" to prevent a lock being held forever if a node crashes. What p95 handoff latency reduction was observed in the Stanford DAWN Lab AgentBench-2 study? The Stanford DAWN Lab AgentBench-2 study saw p95 handoff times drop from 4.2 seconds under polling to 0.6 seconds under event triggers, an 86% reduction. Under what conditions is the persistent event bus considered a mandatory default for multi-agent teams? For teams managing five or more distinct agents, or any workflow enforcing sub-2-minute step SLAs, the persistent event bus is the mandatory default. Quick answers Why does polling cause delays in multi-agent coordination? | Polling leaves every dependency waiting for the next check, so handoffs stall and heterogeneous capabilities cannot chain cleanly. |
| How does push prevent duplicate side effects during failures? | A fail-closed gate suppresses scheduler dispatch when backing store health is uncertain, and redelivery becomes a no-op while tasks remain dispatched or running. |  |  |  |
| What specific mechanism allows heterogeneous agents like Go and TypeScript to interoperate without shared memory? | OpenAI Agents SDK handoff uses a typed envelope carrying capability token plus artifact URI, allowing the receiver to validate the token and dereference the URI. |  |  |  |
| According to the Stanford DAWN Lab data, what is the p95 Handoff Latency for event-driven systems compared to polling? | Event-driven systems have a p95 Handoff Latency of 0.6s, whereas polling has a latency of 4.2s. |  |  |  |
| What is the recommended role for polling in a robust multi-agent architecture? | The robust pattern is push for detection plus poll only as a staleness watchdog fallback if no event occurs for a run_id in 5 minutes. |  |  |  |

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