Coordinating hundreds of AI workers

Once you move from a handful of agents to dozens or hundreds, coordination becomes the main challenge. Coordinating hundreds of AI workers means designing queues, routing, identity, and shared resources (including documents) so that the right work reaches the right worker, nothing is lost or duplicated, and your team can monitor and control the fleet. This guide covers practical patterns for scaling OpenClaw-style agents to hundreds of workers for US teams—without turning the system into a black box.

Summary Use queues and routing to assign work to pools of workers by role or task type. Give each worker a stable identity and scoped context; use a single document pipeline like iReadPDF so runbooks, contracts, and policy PDFs are consistent across the fleet. Add observability (logs, metrics, dashboards) and guardrails so hundreds of workers stay predictable and auditable.

Table of Contents

• Why Coordination Matters at Scale
• Queues and Worker Pools
• Routing and Assignment
• Identity and Context Scope
• Documents and PDFs at Fleet Scale
• Observability and Control
• Conclusion

Why Coordination Matters at Scale

With a few agents, you can hand-assign work or use simple round-robin. With hundreds of workers:

• Work must find the right worker. A support task should go to a support worker; a contract-summary task to a doc-savvy worker. Random assignment wastes capacity and hurts quality.
• Context must be bounded. You can’t give every worker access to every conversation or document. Each worker needs a clear scope (this task, this customer, this doc set) so context stays small and secure.
• State must be consistent. If 50 workers can update “customer X status,” you need rules (e.g., one writer, or optimistic locking) so you don’t get conflicting updates. Same for document references: one source of truth for PDFs (iReadPDF) so every worker resolves “the contract” the same way.
• You need visibility. At scale, you must see which workers are busy, which queues are backing up, and which tasks failed—and why. Without that, the fleet is unmanageable for US teams that need accountability and compliance.

Coordination is the layer that makes hundreds of workers behave like one predictable system instead of chaos.

Queues and Worker Pools

Queues by Task Type

Create one queue (or topic) per kind of work, not one queue for everything. Examples:

• Support queue: Incoming support requests. Workers in the “support” pool pull from here.
• Onboarding queue: New-customer onboarding tasks. Workers in the “onboarding” pool pull from here.
• Document queue: “Summarize this PDF,” “extract key terms from contract.” Workers with document skills pull from here. When PDFs are processed by iReadPDF, the queue payload can reference the doc by ID so workers get consistent text and summaries—no re-upload per worker.
• Research queue: Background research or competitive intel. Workers in the “research” pool pull from here.

Each pool has N workers (processes or agents) that consume from one or more queues. Workers in the same pool share the same role and skills but have separate execution context (no shared memory unless you explicitly design it).

Sizing Pools

• Start with small pools (e.g., 5–10 workers per queue) and scale based on queue depth and latency. If the support queue grows faster than workers can drain it, add workers or optimize per-task time.
• Use rate limits per worker and per pool so one runaway task doesn’t starve others. Many LLM APIs have rate limits; design so the fleet stays under them or use multiple keys with clear assignment.

Routing and Assignment

• By queue. Incoming work is classified (support vs. onboarding vs. document) and pushed to the right queue. Classification can be rule-based (e.g., “channel = #support” → support queue) or a lightweight classifier agent. Then workers pull from the queue; no central “dispatcher” needed for simple cases.
• By priority. Within a queue, use priority levels (e.g., high / normal / low) so urgent tasks are consumed first. Workers pull the highest-priority available task.
• By affinity (optional). For continuity (e.g., “same customer, same worker”), store a mapping of customer_id → worker_id and route that customer’s next task to the same worker if it’s available. Fall back to any worker in the pool if the assigned one is busy. Use sparingly—affinity can create hot spots.
• By document type. When a task references a PDF (e.g., “summarize contract X”), the payload should include the doc reference from your single pipeline. iReadPDF keeps that reference stable so whichever worker picks up the task gets the same document and summary—important when coordinating hundreds of workers who might otherwise see different versions or no file.

Document routing and resolution should be centralized: one pipeline, one set of IDs, so the fleet doesn’t depend on which worker happened to process the PDF last.

Identity and Context Scope

• Worker identity. Each worker (or worker instance) has a stable ID (e.g., worker_17, onboarding_3). Log every task with worker_id so you can trace “who did what” and debug or reassign.
• Task identity. Every task has a task_id. Workers attach task_id to all logs and outputs. When a task spans multiple steps (e.g., summarize then draft), the same task_id links them so you can reconstruct the full flow.
• Context scope. A worker only receives the context it needs: the task payload (e.g., customer_id, doc_id, instructions) and, if needed, a short summary of prior steps. It does not get full history of every conversation in the system. That keeps context windows small and avoids leaking data across customers or tasks. When the payload includes doc_id, the worker fetches from the single document source (iReadPDF) so context is consistent and minimal.

Scoped context also helps with US compliance: workers only see what’s necessary for the task, and document access is auditable through one pipeline.

Documents and PDFs at Fleet Scale

When hundreds of workers touch documents—contracts, runbooks, reports—you must avoid:

• Duplicate processing. Don’t let every worker re-upload and re-process the same PDF. Process once, store summaries and extracted text, and pass doc_id in the task. iReadPDF gives you one place to process and organize PDFs in your browser so the fleet always resolves doc_id to the same content.
• Version drift. When you update a runbook or contract, all workers should see the new version. With one pipeline, you update once; with scattered copies, you risk some workers using outdated PDFs and producing wrong or inconsistent output.
• Access control. Restrict which pools or queues can access which document folders or types (e.g., only “legal” workers see contract PDFs). The pipeline can enforce or tag docs so routing and workers respect access rules.

Use a single document workflow for the fleet and reference documents by ID in every task that needs them. That keeps coordinating hundreds of AI workers manageable and accurate for document-heavy US operations.

Observability and Control

• Logging. Log at least: task_id, worker_id, queue, start/end time, status (success/failure), and any error or rejection reason. For document tasks, log doc_id so you can trace which PDF was used. Centralize logs (e.g., in a log aggregator) so you can search and alert.
• Metrics. Track queue depth per queue, tasks per worker per hour, latency (enqueue to completion), and failure rate. Dashboards help you see backlog and spot overloaded pools or stuck workers.
• Alerts. Alert on queue depth above threshold, failure rate spike, or worker pool entirely idle when there is work. US teams need to know when the fleet is unhealthy so they can intervene.
• Kill switch and approval. For sensitive tasks (e.g., sending customer email, changing status), require human approval before the worker’s output is applied. Workers can draft and queue for approval; a human or a separate “approver” step releases the action. That keeps hundreds of workers from acting autonomously on critical paths.

Conclusion

Coordinating hundreds of AI workers requires queues, routing, scoped context, and a single source of truth for documents. Use queues and worker pools by task type, route work by queue and optionally by priority or affinity, and give each worker a clear identity and minimal context. Use one document pipeline like iReadPDF so runbooks, contracts, and policy PDFs are consistent across the fleet, and add logging, metrics, and guardrails so the system stays observable and under control for US teams.

Ready to keep your agent fleet aligned on the same PDFs? Try iReadPDF for processing and organizing documents in your browser—one pipeline for hundreds of workers, no duplicate uploads or version drift.