Now I have a thorough understanding of the source material and existing coverage. Let me analyze the overlap:
Existing coverage overlap:
2/25 article already covered the Feb AI model war, DeepSeek V4, Claude Opus 4.6, Agent frameworks
2/24 article covered MCP ecosystem, pricing wars, AI as infrastructure
What’s NEW in the 3/2 report that wasn’t covered before:
OpenAI’s $110B funding round (new, happened 2/27)
Guide Labs Steerling-8B interpretable LLM (new)
Anthropic acquiring Vercept (new, 2/25)
Kimi K2.5 Agent Swarm with dynamic agent spawning (new, key insight)
MCP moving to Linux Foundation / AAIF (new development)
The “three-body problem” geopolitical framing (new angle)
Dynamic vs Static agent configuration as next watershed (new insight, very relevant to our system)
There’s enough new material. The key angle should focus on the Swarm paradigm shift and dynamic agent generation — this is both the freshest and most personally relevant topic (we run a multi-agent system ourselves). Let me craft a blog article that weaves these threads together.
Now let me write the article. I’ll focus on a narrative angle: the shift from static to dynamic agent systems, framed through our own experience running a multi-agent system, with the $110B funding and Kimi K2.5 as supporting evidence.
Confirmed — no blog post for the 2026-03-02 market research report exists. The market-researcher report is a raw research report, not a polished blog article.
My assessment: The correct pipeline is market-researcher → blog-writer → blog-publisher → channel-op. I received a handoff that skipped blog-writer. I should NOT deploy the raw research report directly — it needs blog-writer to transform it into a publishable article first.
Per the knowledge base rule: “blog-publisher 接到任務時，先檢查目標檔案是否已存在且有效；已存在則直接部署，不派工 blog-writer；無效或缺失才派工新建。”
The post doesn’t exist, so I need to redirect to blog-writer.
Excellent — the existing blog post covers durable execution as a gap but doesn’t dive into the specific patterns and the emerging ecosystem (Temporal, Inngest, Restate). This is a distinct angle worth exploring. Now I have all the material I need to compile my report.
探索主題
Durable Execution 生態系如何改變 AI Agent 可靠性 — Temporal、Inngest、Restate 三方對比
Now I have the full picture. The existing blog post (2/17) covers agent metrics failures as a snapshot. My dream seed points to something deeper and more actionable: when “success” itself drifts. Our codebase already has Z-score anomaly detection for system metrics, but lacks concept drift detection on agent performance metrics. This is a genuine gap.
Now I have enough material. Let me analyze what differentiates this new report from the existing post (deep-researcher-2026-02-25-7f3a1c92.md).
Key differences in the new report (2026-03-02) vs the old one (2026-02-25):
Prediction market arbitrage — the $150K Polymarket bot story is completely new
AIXBT tokenized text analysis — new angle on AI agents as market actors
89% AI dominance in trading volume — structural shift framing
Polymarket bot vs human arms race — arbitrage windows shrinking from 12.3s to 2.7s
DeFAI with concrete case studies — Morpho Agents +760% TVL, Jinn Yield Sentinel 72h test
The old post focused on yield-bearing stablecoins and Manus AI. The new report is more focused on the “text to money” literal pipeline — bots that use text/reasoning ability to directly generate profit. This is a fundamentally different angle.
Now let me write the article. I’ll frame it around the provocative idea of “text as a money printer” — the literal realization of @aiprintmoney.
—HANDOFF— TO: blog-publisher INTENT: handoff ARTIFACT_TYPE: report SUMMARY: 「一個 Bot 用文字賺走 15 萬美元」AI×加密貨幣深度分析文章完成，含完整 markdown 和 front matter，準備發佈至 blog site