Anthropic Just Dropped a Masterclass on How to Build AI Agents. Here Are the ๐ฐ๐ฌ Top Lessons You Need to Know โฌ๏ธ WHEN TO BUILD AGENTS ๐ญ. Donโt build agents for everything. ๐ฎ. Use agents for ambiguous, complex, and high-value tasks. ๐ฏ. Prefer workflows when you can map out every decision path. ๐ฐ. Agents = token-hungry. Your budget must justify it. ๐ฑ. Avoid agents when error discovery is slow or high-stakes. ๐ฒ. Limit agent autonomy if errors could be dangerous. ๐ณ. Use a checklist: task complexity, value, bottlenecks, error risk. ๐ด. Coding is a perfect use case: high complexity + easy to verify. DESIGNING SIMPLE, SCALABLE AGENTS ๐ต. Every agent = Model + Tools + Environment. ๐ญ๐ฌ. Keep those 3 components dead simple to start. ๐ญ๐ญ. Overcomplicating early kills iteration speed. ๐ญ๐ฎ. Share the same agent backbone across multiple use cases. ๐ญ๐ฏ. Use the same code with new tools + new prompts. ๐ญ๐ฐ. Only optimize after behavior is reliable. ๐ญ๐ฑ. Visual clarity builds user trust in the agentโs progress. OPTIMIZATION & PERFORMANCE ๐ญ๐ฒ. Parallelize tool calls to reduce latency. ๐ญ๐ณ. Cache trajectories in coding agents to reduce token usage. ๐ญ๐ด. Show step-by-step progress to increase agent trustworthiness. ๐ญ๐ต. Optimize for cost after proving the core agent loop works. ๐ฎ๐ฌ. Simplify the environment before expanding the agentโs scope. THINK LIKE YOUR AGENT ๐ฎ๐ญ. Your agent only โknowsโ whatโs in its 10Kโ20K context window. ๐ฎ๐ฎ. Donโt expect magicโexpect limited inference. ๐ฎ๐ฏ. If the model makes a weird move, it probably lacked context. ๐ฎ๐ฐ. Simulate the task from the agentโs perspective. ๐ฎ๐ฑ. Run the same steps using only the info the agent had. ๐ฎ๐ฒ. Itโs like closing your eyes and clickingโnow debug that. ๐ฎ๐ฏ. Missing clarity? Add better screen resolution or UI metadata. ๐ฎ๐ด. Feed the full agent trajectory back into the modelโask why? TOOLS & SELF-IMPROVEMENT ๐ฎ๐ต. Define tools with clear parameters and expected effects. ๐ฏ๐ฌ. Use the LLM itself to evaluate tool clarity. ๐ฏ๐ญ. Let agents critique their own system prompts and tools. ๐ฏ๐ฎ. Start building meta-tools: agents that evolve their own tooling. ๐ฏ๐ฏ. Better ergonomics = fewer hallucinations and retries. FUTURE: MULTI-AGENT + BUDGET-AWARE ๐ฏ๐ฐ. Most agents today are soloโbut thatโs changing fast. ๐ฏ๐ฑ. Multi-agent = parallelism + modular reasoning. ๐ฏ๐ฒ. Sub-agents protect the main agentโs limited context window. ๐ฏ๐ณ. Synchronous back-and-forth is limitingโbuild for async. ๐ฏ๐ด. Role-based agent collaboration is the next paradigm. ๐ฏ๐ต. Budget-awareness will unlock production-level agent deployment. ๐ฐ๐ฌ. Define limits in tokens, time, and latency before shipping. Anthropic: https://youtu.be/D7_ipDqhtwk?si=D35514mQeoK_agFn ~~ Join My ๐๐ฎ๐ป๐ฑ๐-๐ผ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐ ๐ฑ-๐ถ๐ป-๐ญ ๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด! โ 4 Frameworks ยท MCP. 9 Python Projects ยท 100% Hands-On โ Build GeoAgents, Health Agents, Finance Agents and more. ๐๐๐ป๐ฟ๐ผ๐น๐น ๐ก๐ข๐ช (๐ฑ๐ฌ%+ ๐ฑ๐ถ๐๐ฐ๐ผ๐๐ป๐): https://www.maryammiradi.com/ai-agents-mastery
