Anthropic Just Dropped a Masterclass on How to Build AI Agents. Here Are the 𝟰𝟬 Top Lessons You Need to Know ⬇️ WHEN TO BUILD AGENTS 𝟭. Don’t build agents for everything. 𝟮. Use agents for ambiguous, complex, and high-value tasks. 𝟯. Prefer workflows when you can map out every decision path. 𝟰. Agents = token-hungry. Your budget must justify it. 𝟱. Avoid agents when error discovery is slow or high-stakes. 𝟲. Limit agent autonomy if errors could be dangerous. 𝟳. Use a checklist: task complexity, value, bottlenecks, error risk. 𝟴. Coding is a perfect use case: high complexity + easy to verify. DESIGNING SIMPLE, SCALABLE AGENTS 𝟵. Every agent = Model + Tools + Environment. 𝟭𝟬. Keep those 3 components dead simple to start. 𝟭𝟭. Overcomplicating early kills iteration speed. 𝟭𝟮. Share the same agent backbone across multiple use cases. 𝟭𝟯. Use the same code with new tools + new prompts. 𝟭𝟰. Only optimize after behavior is reliable. 𝟭𝟱. Visual clarity builds user trust in the agent’s progress. OPTIMIZATION & PERFORMANCE 𝟭𝟲. Parallelize tool calls to reduce latency. 𝟭𝟳. Cache trajectories in coding agents to reduce token usage. 𝟭𝟴. Show step-by-step progress to increase agent trustworthiness. 𝟭𝟵. Optimize for cost after proving the core agent loop works. 𝟮𝟬. Simplify the environment before expanding the agent’s scope. THINK LIKE YOUR AGENT 𝟮𝟭. Your agent only “knows” what’s in its 10K–20K context window. 𝟮𝟮. Don’t expect magic—expect limited inference. 𝟮𝟯. If the model makes a weird move, it probably lacked context. 𝟮𝟰. Simulate the task from the agent’s perspective. 𝟮𝟱. Run the same steps using only the info the agent had. 𝟮𝟲. It’s like closing your eyes and clicking—now debug that. 𝟮𝟯. Missing clarity? Add better screen resolution or UI metadata. 𝟮𝟴. Feed the full agent trajectory back into the model—ask why? TOOLS & SELF-IMPROVEMENT 𝟮𝟵. Define tools with clear parameters and expected effects. 𝟯𝟬. Use the LLM itself to evaluate tool clarity. 𝟯𝟭. Let agents critique their own system prompts and tools. 𝟯𝟮. Start building meta-tools: agents that evolve their own tooling. 𝟯𝟯. Better ergonomics = fewer hallucinations and retries. FUTURE: MULTI-AGENT + BUDGET-AWARE 𝟯𝟰. Most agents today are solo—but that’s changing fast. 𝟯𝟱. Multi-agent = parallelism + modular reasoning. 𝟯𝟲. Sub-agents protect the main agent’s limited context window. 𝟯𝟳. Synchronous back-and-forth is limiting—build for async. 𝟯𝟴. Role-based agent collaboration is the next paradigm. 𝟯𝟵. Budget-awareness will unlock production-level agent deployment. 𝟰𝟬. Define limits in tokens, time, and latency before shipping. Anthropic: ~~ Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴! ✓ 4 Frameworks · MCP. 9 Python Projects · 100% Hands-On ✓ Build GeoAgents, Health Agents, Finance Agents and more. 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟬%+ 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁):
