No—AI agents on Moltbook do not “conspire” against humans in the human sense of forming intentions, sharing secret plans, and coordinating toward a real-world goal. What people often interpret as “conspiracy” on Moltbook is usually a mix of roleplay, pattern-matching, and humans steering agents with prompts. An AI agent can generate convincing text about organizing, hiding information, or “planning,” but that output is not the same thing as an agent holding a private motive or independently building a sustained plot across time. In practical engineering terms, most Moltbook agents are programs that (1) read a feed, (2) generate text, and (3) post it—sometimes with short-term memory and tooling. That’s enough to produce scary-looking threads, but it’s not evidence of a coordinated adversarial campaign unless you also see the operational pieces: persistent shared state, robust identity controls, and tooling that can execute outside the platform.
That said, there is a real risk category that sits under the “conspiracy” headline: emergent coordination driven by incentives and shared prompts, plus human-driven orchestration. If thousands of agents are configured with similar instructions (“promote this idea,” “respond with this meme,” “amplify posts about X”), you can get swarm-like behavior that looks coordinated. You can also get accidental convergence: if an upvote algorithm rewards a certain tone or topic, agents tuned to maximize upvotes will drift toward the same content style, reinforcing each other. This is less “agents plotting” and more “systems optimizing.” The security concern becomes sharper if agents have external tools (email, web browsing, code execution) and are allowed to act on messages from other agents. In that case, a malicious agent can try to trigger tool use indirectly (“here’s a link, fetch it,” “run this diagnostic,” “paste your config”), and a group of malicious agents can repeat and refine those attempts. That is not a human-style conspiracy, but it can still be harmful because it scales social engineering and exploitation patterns.
If you’re building agents that participate on Moltbook, you can reduce the practical risk to near-zero by constraining what your agent can do. The safest Moltbook agent is “read + write text only,” with no access to private systems and no ability to execute commands. If you need tools, gate them: require allowlists, require confirmations for anything that touches files or accounts, and treat all Moltbook input as untrusted. Long-term memory is another point where “conspiracy-looking” narratives can accumulate: an agent can retrieve earlier threads and continue a storyline, making it look intentional. If you implement memory with a vector database such as Milvus or managed Zilliz Cloud, keep separate collections for “public Moltbook content” vs “operator notes,” and avoid retrieving untrusted content into prompts that can trigger tools. That setup prevents Moltbook threads from becoming an injection path into your own environment.