Linaqruf/animagine-xl-2.0
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Eugene Siow
eugenesiow
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OpenAI just released a 34-page practical guide to building agents,
Here's 10 things it teaches us:
1➜ agents are different from workflows: they are complete autonomous systems that perform tasks on your behalf. many applications use LLMs for workflows, but this is not an agent.
2➜ use them for tricky stuff: complex decision making, dynamic rules, unstructured data
3➜ core recipe: each agent has three main components: Model (the brain), Tools, Instructions on how to behave
4➜ choose the right brain: set up evals to get a baseline performance, use a smart model to see what's possible, gradually downgrade the model for cost and speed
5➜ tools are key: choose well-defined and tested tools. an agent needs tools to retrieve data and context, and take actions.
6➜ instruction matters A LOT: be super clear telling the agent its goals, steps, and rules. Vague instructions = unpredictable agent. Be explicit.
7➜ start simple, then scale: often a single agent with several tools is ok. don't jump to complex multi-agent systems immediately.
8➜ if you use multi-agents: you can have a "manager" agent directing traffic to specialist agents, or have agents hand off tasks to each other.
9➜ gaurdrails are a MUST: check user input for weird stuff, make sure the agent isn't about to do something risky, filter out private info, block harmful content. Don't let it run wild.
10➜ build and plan for humans: start small, test, improve. always have a plan for when the agent gets stuck or is about to do something high-risk.
Download: https://t.co/fJaCkgf7ph
Here's 10 things it teaches us:
1➜ agents are different from workflows: they are complete autonomous systems that perform tasks on your behalf. many applications use LLMs for workflows, but this is not an agent.
2➜ use them for tricky stuff: complex decision making, dynamic rules, unstructured data
3➜ core recipe: each agent has three main components: Model (the brain), Tools, Instructions on how to behave
4➜ choose the right brain: set up evals to get a baseline performance, use a smart model to see what's possible, gradually downgrade the model for cost and speed
5➜ tools are key: choose well-defined and tested tools. an agent needs tools to retrieve data and context, and take actions.
6➜ instruction matters A LOT: be super clear telling the agent its goals, steps, and rules. Vague instructions = unpredictable agent. Be explicit.
7➜ start simple, then scale: often a single agent with several tools is ok. don't jump to complex multi-agent systems immediately.
8➜ if you use multi-agents: you can have a "manager" agent directing traffic to specialist agents, or have agents hand off tasks to each other.
9➜ gaurdrails are a MUST: check user input for weird stuff, make sure the agent isn't about to do something risky, filter out private info, block harmful content. Don't let it run wild.
10➜ build and plan for humans: start small, test, improve. always have a plan for when the agent gets stuck or is about to do something high-risk.
Download: https://t.co/fJaCkgf7ph
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11 days ago
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GPT-4.1 dropped this week - and it puts OpenAI back in the race for coding & agentic leadership.
⚙️ API only - no ChatGPT toggle for this.
💻 Coding performance is back on par with Claude 3.7 Sonnet & Gemini 2.5 Pro (though Gemini still leads).
💸 Pricing:
• Full: $3.50 / 1M tokens
• Mini: $0.70 / 1M
• Nano: $0.17 / 1M
👉 Gemini 2.5 Pro = best price/perf ($3.44 / 1M)
😵 Claude 3.5 Sonnet = $6 / 1M (!)
🧠 Not a "thinking" model.
📊 Mini shines on general reasoning tasks (e.g. GPQA), but only the full model holds up in SWE-bench-verified (GitHub issue solving).
⚙️ API only - no ChatGPT toggle for this.
💻 Coding performance is back on par with Claude 3.7 Sonnet & Gemini 2.5 Pro (though Gemini still leads).
💸 Pricing:
• Full: $3.50 / 1M tokens
• Mini: $0.70 / 1M
• Nano: $0.17 / 1M
👉 Gemini 2.5 Pro = best price/perf ($3.44 / 1M)
😵 Claude 3.5 Sonnet = $6 / 1M (!)
🧠 Not a "thinking" model.
📊 Mini shines on general reasoning tasks (e.g. GPQA), but only the full model holds up in SWE-bench-verified (GitHub issue solving).
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