# Building ArcticSwarm from Scratch: A Production-Grade Multi-Agent Deep Research System > Source: > Published: 2026-07-15 04:28:07+00:00 On June 2, 2026, Snowflake AI Research published an engineering blog post introducing **ArcticSwarm** — a multi-agent system that transforms how enterprises conduct hybrid deep research across structured databases and unstructured web sources. ArcticSwarm is a multi-agent deep research framework introduced by Snowflake AI Research that addresses a key enterprise AI challenge: combining structured evidence stored in SQL databases with unstructured information spread across web pages, documentation, and other external sources. Instead of relying on a single reasoning agent — which can suffer from confirmation bias — or loosely coordinated agent pools that may converge prematurely on the same conclusions, ArcticSwarm coordinates up to 16 specialized agents for browsing, coding, SQL analysis, and reasoning through a Gated Bulletin Board System (BBS). The framework operates in three governance stages: According to Snowflake AI Research, ArcticSwarm significantly improves performance on hybrid enterprise research tasks compared with single-agent approaches, demonstrating the value of combining independent exploration, collaborative verification, and evidence-gated synthesis. The architecture is designed for enterprise scenarios where trustworthy answers require integrating both internal structured data and external knowledge sources. The core insight is powerful: traditional AI agents fail at enterprise research because they either fall into **confirmation bias** (anchoring on the first lead) or **groupthink** (multiple agents collapsing onto one hypothesis). ArcticSwarm solves this with a novel coordination mechanism called the **Gated Bulletin Board System (BBS)**, which forces agents to explore independently before collaborating. In this article, I’ll walk through my end-to-end implementation of the ArcticSwarm architecture — deployed locally with Docker, running on free-tier LLMs, with a Streamlit UI and 24 passing tests. **Full Implementation plan on GitHub Repository:** [satishkumarai/arcticswarm](https://github.com/satishkumarai/arcticswarm) *The ArcticSwarm research dashboard — users log in, configure agent settings, and submit complex research queries that span both databases and the web.* According to Snowflake’s research, traditional multi-agent setups fall into three structural traps: ArcticSwarm defeats these with **three governance modes** enforced through a central Bulletin Board: ``` | Mode | Mode Name | Rule || ------ | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- || Mode 1 | Isolation | Agents can WRITE to the BBS but cannot READ, forcing independent exploration and preventing bias from other agents' findings. || Mode 2 | Collaboration | Agents can READ from and WRITE to the BBS, enabling cross-examination, knowledge sharing, and collaborative refinement of findings. || Mode 3 | Synthesis | Only the Orchestrator writes to the BBS, consolidating verified findings into the final validated report. | ``` The **Hybrid Evidence Gate** then blocks final output until configurable evidence thresholds are met (e.g., at least 2 SQL evidence posts + 2 web evidence posts + 1 cross-domain synthesis). *Reference: **ArcticSwarm: Transforming Hybrid Deep Research for Enterprise Intelligence** — Snowflake AI Research, June 2, 2026* Here’s what I built: The original ArcticSwarm architecture uses LLM tool calling for agent-tool interaction. However, on free-tier LLMs (Groq), tool calling is unreliable. I discovered that agents would **hallucinate evidence** — fabricating URLs and SQL results — because the LLM never actually executed any tools. My solution: the **Retrieve-Then-Analyze** pattern. Instead of: ``` LLM decides to call tool → tool executes → LLM analyzes result ``` I implemented: ``` Agent executes tool directly → Real results posted to BBS → Single LLM synthesizes all evidence ``` This means: The result: **real URLs from real web searches** instead of hallucinated sources. The BBS is the heart of ArcticSwarm. All inter-agent communication flows through it, with access enforced structurally: ``` python class GatedBBS: async def post(self, task_id, agent_id, post, current_mode): """Mode 3: Only orchestrator can write.""" if current_mode == GovernanceMode.SYNTHESIS and agent_id != "orchestrator": raise PermissionError("Mode 3: Only orchestrator can post") # ... write to Redisasync def read(self, task_id, agent_id, current_mode): """Mode 1: Read access DENIED for agents.""" if current_mode == GovernanceMode.WRITE_ONLY and agent_id != "orchestrator": raise PermissionError("Mode 1: Agents cannot read BBS") # ... read from Redis ``` This isn’t just a prompt instruction — it’s **architectural enforcement**. An agent in Mode 1 physically cannot read the BBS, regardless of what the LLM “decides.” ``` php class BrowsingAgent(BaseAgent): async def run(self, instruction: str) -> list[BBSPost]: # Extract core query from instruction search_query = instruction.split("on the web:")[-1].strip()# Step 1: Execute DuckDuckGo search directly (NO LLM call) browser = WebBrowserTool() search_results = await browser.web_search(search_query, num_results=5) # Step 2: Format results as structured evidence findings = "\n".join( f"- [{r['title']}]({r['url']}): {r['snippet']}" for r in search_results ) # Step 3: Post REAL results to BBS with actual URLs post = BBSPost( evidence_type=EvidenceType.WEB_FINDING, content=f"Web search for: {search_query}\n\nFindings:\n{findings}", sources=[r["url"] for r in search_results], # Real URLs! confidence=0.85, ) await self.bbs.post(self.task_id, self.agent_id, post, self.governance_mode) return [post] ``` Before generating the final report, the gate checks: ``` php class HybridEvidenceGate: async def check(self, bbs, task_id) -> GateResult: summary = await bbs.get_evidence_summary(task_id) gaps = [] if len(sql_posts) < self.config.min_sql_evidence: gaps.append(f"Need more SQL evidence") if len(web_posts) < self.config.min_web_evidence: gaps.append(f"Need more web evidence") if self.config.require_synthesis and not synthesis_posts: gaps.append("Missing cross-domain synthesis") return GateResult(passed=len(gaps) == 0, gaps=gaps) php class Orchestrator: async def research(self, query: str) -> ResearchReport: # Phase 1: Deterministic planning (no LLM) plan = self._default_plan(query) # Phase 2: Mode 1 — Isolated exploration await self.bbs.set_mode(task_id, GovernanceMode.WRITE_ONLY) # Agents search/query independently # Phase 3: Mode 2 — Collaborative review await self.bbs.set_mode(task_id, GovernanceMode.READ_WRITE) # Reasoning agent compiles evidence # Phase 4: Evidence Gate + Synthesis gate_result = await self._evidence_gate.check(self.bbs, task_id) report = await self._generate_report(query, evidence, gate_result) # ^ This is the ONLY LLM call ``` The synthesis prompt explicitly guards against fabrication: ``` prompt = ( "Generate a research report based ONLY on the evidence below.\n\n" "CRITICAL RULES:\n" "- ONLY cite URLs and sources that appear in the evidence\n" "- NEVER invent URLs, database names, or data values\n" "- If evidence is weak or missing, explicitly state that\n" "- Do NOT fabricate any information\n\n" f"ALL EVIDENCE ({len(evidence)} posts):\n{evidence_text}") ``` Before this fix, the system returned fabricated URLs like www.snowflake_arctic_llm.com. After the fix, it returns real DuckDuckGo results: ``` { "sources": [ "https://www.snowflake.com/en/product/features/arctic/", "https://www.snowflake.com/en/blog/arctic-open-efficient-foundation-language-models-snowflake/", "https://developer.nvidia.com/blog/new-llm-snowflake-arctic-model-for-sql-and-code-generation/", "https://github.com/Snowflake-Labs/snowflake-arctic" ]} ``` *The **/task/{id}/evidence endpoint showing real URLs retrieved from DuckDuckGo — no fabricated sources.* ``` git clone https://github.com/satishkumarai/arcticswarm.gitcd arcticswarmcp .env.example .env# Add your GROQ_API_KEY to .envmake up ``` Open http://localhost:8501, log in with demo / demo123, and submit a research query. ``` make test# 24 tests pass: BBS governance, evidence gate, orchestrator, integration ``` *Full test suite verifying BBS governance enforcement, evidence gate thresholds, orchestrator lifecycle, and end-to-end integration.* ``` | Metric | Value || ------------------- | ------------------- || Total files | 53 (+ this article) || Test coverage | 24 tests passing || LLM calls per query | 1 (synthesis only) || Query latency | 15–40 seconds || Cost per query | $0 (Groq free tier) || Hallucination rate | 0% (verified URLs) | ``` *This article represents the author’s personal views and experience, not those of any employer.* 👏 Give it a clap if it added value 🔗 Share it with your team ➕ Follow for more 📘 Medium: [Satish Kumar](https://medium.com/u/d170d49944ec?source=post_page---user_mention--7250f265dee7---------------------------------------) 🔗 LinkedIn: [satishkumar-snowflake](https://www.linkedin.com/in/satishkumar-snowflake/) See you in the next one! 👋 *GitHub: **satishkumarai/arcticswarm* [Building ArcticSwarm from Scratch: A Production-Grade Multi-Agent Deep Research System](https://pub.towardsai.net/building-arcticswarm-from-scratch-a-production-grade-multi-agent-deep-research-system-d03c25365b7e) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.