𝘌𝘷𝘦𝘳𝘺𝘰𝘯𝘦'𝘴 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴.
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 𝘫𝘰𝘣𝘴."
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨."
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦."
However, the reality is far more complex. AI agents are not just chat interfaces or simple API calls. These are full-stack engineering beasts that require integration and coordination across multiple layers of technology. The user may see it as simple, but under the hood ("iceberg") it's anything but simple and complex infrastructure.
𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️
1️⃣ 𝗙𝗿𝗼𝗻𝘁-𝗲𝗻𝗱 – The user interface, but that's just the surface.
2️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆 – Managing short-term and long-term context.
3️⃣ 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – Identity verification, security, and access control.
4️⃣ 𝗧𝗼𝗼𝗹𝘀 – External plugins, search capabilities, integrations.
5️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Monitoring, logging, and performance tracking.
6️⃣ 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – Multi-agent coordination, execution, automation.
7️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 – Directing queries to the right AI models.
8️⃣ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 – The LLMs that power the agent's reasoning.
9️⃣ 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Data ingestion and processing pipelines.
🔟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – Vector stores and structured storage for knowledge retention.
1️⃣1️⃣ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲/𝗕𝗮𝘀𝗲 – Compute environments and cloud execution.
1️⃣2️⃣ 𝗖𝗣𝗨/𝗚𝗣𝗨 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 – The backbone of AI model execution.
In summary, AI agents aren't just "smart chatbots" — they're full-stack AI systems requiring seamless orchestration across multiple layers. In addition, there are many companies developing similar technologies right now, fueled by hype and funding. I believe that the market won't stay this fragmented — consolidation is inevitable (I believe if you come back to this post in 3 years 70% of the companies below will basically not exist anymore).
𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀? 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗯𝗿𝗶𝗱𝗴𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲 𝗯𝘆 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗨𝗫 𝘀𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗳𝗼𝗿 𝘂𝘀𝗲𝗿𝘀.
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 𝘫𝘰𝘣𝘴."
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨."
- "𝘈𝘨𝘦𝘯𝘵𝘴 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦."
However, the reality is far more complex. AI agents are not just chat interfaces or simple API calls. These are full-stack engineering beasts that require integration and coordination across multiple layers of technology. The user may see it as simple, but under the hood ("iceberg") it's anything but simple and complex infrastructure.
𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️
1️⃣ 𝗙𝗿𝗼𝗻𝘁-𝗲𝗻𝗱 – The user interface, but that's just the surface.
2️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆 – Managing short-term and long-term context.
3️⃣ 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – Identity verification, security, and access control.
4️⃣ 𝗧𝗼𝗼𝗹𝘀 – External plugins, search capabilities, integrations.
5️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Monitoring, logging, and performance tracking.
6️⃣ 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – Multi-agent coordination, execution, automation.
7️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 – Directing queries to the right AI models.
8️⃣ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 – The LLMs that power the agent's reasoning.
9️⃣ 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Data ingestion and processing pipelines.
🔟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – Vector stores and structured storage for knowledge retention.
1️⃣1️⃣ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲/𝗕𝗮𝘀𝗲 – Compute environments and cloud execution.
1️⃣2️⃣ 𝗖𝗣𝗨/𝗚𝗣𝗨 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 – The backbone of AI model execution.
In summary, AI agents aren't just "smart chatbots" — they're full-stack AI systems requiring seamless orchestration across multiple layers. In addition, there are many companies developing similar technologies right now, fueled by hype and funding. I believe that the market won't stay this fragmented — consolidation is inevitable (I believe if you come back to this post in 3 years 70% of the companies below will basically not exist anymore).
𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀? 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗯𝗿𝗶𝗱𝗴𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲 𝗯𝘆 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗨𝗫 𝘀𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗳𝗼𝗿 𝘂𝘀𝗲𝗿𝘀.
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