# Kimi Sub-Agent Swarms for Parallel Research | Dexity > Use Kimi's Agent Swarm to run up to 100 parallel sub-agents on large research jobs, from 200 companies to 500 papers. AI at Work # Using Kimi's Sub-Agent Swarms for Large-Scale Parallel Research When a job is big and parallelizable — research 200 companies, review 500 papers, enrich a whole dataset — Kimi's Agent Swarm is the tool. It self-directs up to 100 sub-agents in parallel (and scales to hundreds coordinating thousands of steps), cutting execution time on large-scale research, long-form writing, and batch work by up to ~4.5×. Add cheap tokens and the math changes: work you'd never staff or pay frontier rates for becomes a single overnight run. Here's what to point it at, how to structure a swarm, and how to keep the output trustworthy. 26 July 2026 8 min read Author [Dipen Dadhaniya](https://www.linkedin.com/in/dipen-dadhaniya/) Sr. Engineering Manager, Dexity ![A researcher relaxes with a mug at night while an orchestrator robot directs a swarm of small AI sub-agents working through a wall grid of research tasks](/assets/intel-kimi-for-batch-research-hero-8df6f4.webp) On this page 1. [What is Kimi's Agent Swarm good for?](#what-is-kimi-s-agent-swarm-good-for) 2. [Why Kimi fits this job](#why-kimi-fits-this-job) 3. [Where it wins](#where-it-wins) 4. [How to run a swarm well](#how-to-run-a-swarm-well) 5. [Where NOT to use it](#where-not-to-use-it) 6. [FAQ](#faq) ## What is Kimi's Agent Swarm good for? Big, parallelizable jobs — where the work is the same shape repeated across many items. Kimi K2.5 can self-direct **up to 100 sub-agents working in parallel**, each using tools independently, and K2.6's Agent Swarm scales to **hundreds of domain-specialized sub-agents coordinating thousands of steps** in one autonomous run. On large-scale research, long-form writing, and batch tasks, that parallelism cuts execution time by **up to ~4.5×** — and because Kimi's tokens are cheap, running a swarm across a whole workload is actually affordable. New to Kimi? Start with [How to Start Using Kimi](/intel/how-to-use-kimi-2026); this guide is about the swarm/batch use case. * * * ## Why Kimi fits this job - **Parallelism collapses wall-clock time.** A hundred items done one-at-a-time is a slog; a hundred sub-agents working at once finishes in a fraction of the time. That ~4.5× isn't marginal — it's the difference between a project and an overnight job. - **Cost makes breadth affordable.** Parallel agents multiply token usage. On a premium model that's prohibitive; on Kimi it's a line item you can actually approve. - **Tool-using sub-agents.** Each agent can search, read, and call tools independently, so a swarm can _gather_ — not just reason over what you already have. The pattern that unlocks a swarm is **"same task, many items."** If you can describe one unit of work precisely (research one company, summarize one paper, enrich one record), you can fan it out across hundreds. The scarce skill becomes _decomposition_ — turning a big fuzzy goal into a clean, repeatable unit — not the AI itself. * * * ## Where it wins - **Market & competitive research at scale** — profile every company in a category, every competitor's pricing, every player in a market — in parallel, into one structured table. - **Literature and source reviews** — fan out across hundreds of papers or articles, each summarized to the same template, then synthesized. - **Dataset enrichment / classification** — label, categorize, or enrich thousands of records where each item is an independent judgment. - **Large content production** — draft many pages, variants, or localized versions in parallel, then edit down. - **Broad discovery sweeps** — search many angles at once (by entity, by time, by source) to surface what a single query would miss. * * * ## How to run a swarm well 1. **Define the unit of work precisely.** One agent, one item, one clear output shape. Ambiguity multiplied across 100 agents is 100× the mess. 2. **Give each sub-agent a role and its tools.** "Research this company's pricing and headcount, return this JSON" beats "look into this company." Scope tightly. 3. **Standardize the output for aggregation.** Same schema per item so results merge into one table/report cleanly — the aggregation is where the value lands. 4. **Sample and verify before you trust the whole batch.** Spot-check a handful against ground truth; parallel scale means an error in the unit prompt is an error everywhere. 5. **Watch cost and cap scope.** Cheap per item still adds up at hundreds of items — instrument it, and don't silently run a swarm bigger than the job needs. * * * ## Where NOT to use it - **Work that isn't parallelizable.** If step 2 depends on step 1's judgment for the _same_ item, a swarm doesn't help — that's a single long-horizon agent job. - **High-stakes outputs without verification.** Parallel scale multiplies unchecked errors; build the check into the unit. - **Small jobs.** A handful of items doesn't justify orchestrating a swarm — just run them. * * * ## FAQ ### What is Kimi's Agent Swarm? A multi-agent capability where Kimi self-directs many sub-agents in parallel — up to 100 (K2.5), scaling to hundreds coordinating thousands of steps (K2.6) — each using tools independently, cutting time on large-scale research and batch work by up to ~4.5×. ### When should I use a sub-agent swarm? When the job is "the same task across many items" — profiling hundreds of companies, reviewing hundreds of papers, enriching thousands of records. Define one clean unit of work, fan it out, standardize the output, and verify a sample. ### How do I keep swarm output trustworthy? Give each sub-agent a tightly-scoped role and a fixed output schema, sample-check results against ground truth before trusting the batch, and instrument cost so scale stays intentional. * * * _Agent Swarm capabilities (100 parallel sub-agents; hundreds coordinating thousands of steps; ~4.5× speedup) per Moonshot AI and independent coverage of Kimi K2.5/K2.6 — treat as directional and verify current specs. · Dexity.com_ ## More in AI at Work [All Intel →](/intel) [AI at Work ### AI Agent Frameworks in 2026: LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK, Compared There's no single "best" AI agent framework — the right pick depends on your stack and how much control you need. In 2026 the main options are LangGraph (graph-based, most control, strongest for production and complex multi-agent), CrewAI (role-based "crews," fastest to prototype), AutoGen/AG2 (conversation-driven multi-agent), and the OpenAI Agents SDK (lightweight, handoff-based, Python + TypeScript), plus LlamaIndex Workflows, Google ADK, Pydantic AI (type-safe), and the Microsoft Agent Framework (the AutoGen + Semantic Kernel successor). Many teams skip frameworks and build agents with the model SDK plus a loop — Anthropic and Microsoft both recommend starting there. This guide compares them on language, abstraction, multi-agent support, state/memory, and best-fit, with version and maturity claims attributed and dated (they drift). 17 September 2026 · 12 min read](/intel/ai-agent-frameworks-2026) [AI at Work ### How to Build an AI Agent in 2026: A Practical Guide for Engineers You build an AI agent by wrapping a large language model in a loop: the model gets a goal, decides an action, calls a tool, reads the result, and repeats until the task is done. The core pieces are a controller model, tools defined as JSON schemas, a memory system, an orchestration loop, and a termination condition. Tools are exposed through function-calling APIs — the model returns a structured call, your code runs it, and you feed the output back. The hard part isn't the loop; it's knowing when NOT to build an agent. Most production systems are workflows with predefined paths, not autonomous agents — and Gartner projects over 40% of agentic-AI projects will be canceled by end of 2027 on cost and unclear value. This guide walks the architecture, tool calling, memory, patterns, failure modes, and deployment. 17 September 2026 · 12 min read](/intel/how-to-build-an-ai-agent-2026) [AI at Work ### How to Evaluate AI Agents: Trajectories, Tool Use, and Task Success (2026 Guide) Evaluating an AI agent means judging both what it produced and how it got there. Unlike a single LLM call, an agent plans, calls tools, and takes many steps — so a correct final answer can hide a broken path. Combine outcome evaluation (task success rate, final-answer correctness) with trajectory evaluation (tool selection, tool-argument accuracy, step efficiency, error recovery). Score deterministic things with code and reserve LLM-as-judge for subjective quality — while guarding against its position, verbosity, and self-preference biases. Track cost, latency, and step count in the same traces as quality. Run offline evals on a fixed dataset before shipping, then monitor online in production and convert every failure into a permanent regression test. This guide covers the taxonomy, the real agent benchmarks (τ-bench, WebArena, GAIA, SWE-bench, BrowseComp), the tooling, and the mistakes. 17 September 2026 · 12 min read](/intel/how-to-evaluate-ai-agents-2026) Canonical: https://stg.dexity.com/intel/kimi-for-batch-research-2026