Guidelines for Using AI Agents

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? 𝗗𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵—𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙯𝙚. Too often, teams rush to roll out AI agents without a solid deployment playbook. The result? Confused users, poor performance, and broken context threads. Here are 𝗳𝗶𝘃𝗲 𝗯𝗮𝘁𝘁𝗹𝗲-𝘁𝗲𝘀𝘁𝗲𝗱 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 I swear by for deploying AI agents that 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 value (𝘐𝘯𝘧𝘰𝘨𝘳𝘢𝘱𝘩𝘪𝘤 𝘣𝘦𝘭𝘰𝘸 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘭𝘦𝘢𝘳𝘯𝘦𝘳𝘴!) ↳ 𝟭. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 → Define the AI agent’s purpose, tasks, boundaries, and goals.  DO: Be specific. DON’T: Deploy vague, general-purpose agents without direction. ↳ 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Can your agent handle traffic spikes or real-world stress? DO: Run load tests, evaluate metrics, and scale infrastructure. DON’T: Assume your MVP setup will survive in production. ↳ 𝟯. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 → Context is king—especially in AI conversations. DO: Use memory + Retrieval-Augmented Generation (RAG). DON’T: Let agents lose track of user interactions or history. ↳ 𝟰. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 → Deployment isn’t the finish line—it’s the start of learning. DO: Monitor in real-time, collect feedback, evaluate data. DON’T: Rely on pre-launch assumptions or ignore post-launch signals. ↳ 𝟱. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗼𝗳 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 → Your AI agent should evolve. DO: Continuously refine based on real-world usage. DON’T: Treat deployment as “done.” Whether you're building internal copilots or customer-facing agents, following these principles ensures your deployment is not just functional — but 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭. Which principle resonates most with your AI roadmap?

Stop building AI agents in random steps, scalable agents need a structured path. A reliable AI agent is not built with prompts alone, it is built with logic, memory, tools, testing, and real-world infrastructure. Here’s a breakdown of the full journey - 1️⃣ Pick an LLM Choose a reasoning-strong model with good tool support so your agent can operate reliably in real environments. 2️⃣ Write System Instructions Define the rules, tone, and boundaries. Clear instructions make the agent consistent across every workflow. 3️⃣ Connect Tools & APIs Link your agent to the outside world - search, databases, email, CRMs, internal systems - to make it actually useful. 4️⃣ Build Multi-Agent Systems Split work across focused agents and let them collaborate. This boosts accuracy, reliability, and speed. 5️⃣ Test, Version & Optimize Version your prompts, A/B test, keep backups, and keep improving - this is how production agents stay stable. 6️⃣ Define Agent Logic Outline how the agent thinks, plans, and decides step-by-step. Good logic prevents unpredictable behavior. 7️⃣ Add Memory (Short + Long Term) Enable your agent to remember past conversations and user preferences so it gets smarter with every interaction. 8️⃣ Assign a Specific Job Give the agent a narrow, outcome-driven task. Clear scope = better results. 9️⃣ Add Monitoring & Feedback Track errors, latency, failures, and real-world performance. User feedback is the fuel of improvement. 🔟 Deploy & Scale Move from prototype to production with proper infra—containers, serverless, microservices. AI agents don’t scale because of prompts, they scale because of architecture. If you get logic, memory, tools, and infra right, your agents become reliable, predictable, and production-ready. #AI

We’re entering an era where AI isn’t just answering questions — it’s starting to take action. From booking meetings to writing reports to managing systems, AI agents are slowly becoming the digital coworkers of tomorrow!!!! But building an AI agent that’s actually helpful — and scalable — is a whole different challenge. That’s why I created this 10-step roadmap for building scalable AI agents (2025 Edition) — to break it down clearly and practically. Here’s what it covers and why it matters: - Start with the right model Don’t just pick the most powerful LLM. Choose one that fits your use case — stable responses, good reasoning, and support for tools and APIs. - Teach the agent how to think Should it act quickly or pause and plan? Should it break tasks into steps? These choices define how reliable your agent will be. - Write clear instructions Just like onboarding a new hire, agents need structured guidance. Define the format, tone, when to use tools, and what to do if something fails. - Give it memory AI models forget — fast. Add memory so your agent remembers what happened in past conversations, knows user preferences, and keeps improving. - Connect it to real tools Want your agent to actually do something? Plug it into tools like CRMs, databases, or email. Otherwise, it’s just chat. - Assign one clear job Vague tasks like “be helpful” lead to messy results. Clear tasks like “summarize user feedback and suggest improvements” lead to real impact. - Use agent teams Sometimes, one agent isn’t enough. Use multiple agents with different roles — one gathers info, another interprets it, another delivers output. - Monitor and improve Watch how your agent performs, gather feedback, and tweak as needed. This is how you go from a working demo to something production-ready. - Test and version everything Just like software, agents evolve. Track what works, test different versions, and always have a backup plan. - Deploy and scale smartly From APIs to autoscaling — once your agent works, make sure it can scale without breaking. Why this matters: The AI agent space is moving fast. Companies are using them to improve support, sales, internal workflows, and much more. If you work in tech, data, product, or operations — learning how to build and use agents is quickly becoming a must-have skill. This roadmap is a great place to start or to benchmark your current approach. What step are you on right now?

Whether you're using Replit Agent, Assistant, or other AI tools, clear communication is key. Effective prompting isn't magic; it's about structure, clarity, and iteration. Here are 10 principles to guide your AI interactions: 🔹 Checkpoint: Build iteratively. Break down large tasks into smaller, testable steps and save progress often. 🔹 Debug: Provide detailed context for errors – error messages, code snippets, and what you've tried. 🔹 Discover: Ask the AI for suggestions on tools, libraries, or approaches. Leverage its knowledge base. 🔹 Experiment: Treat prompting as iterative. Refine your requests based on the AI's responses. 🔹 Instruct: State clear, positive goals. Tell the AI what to do, not just what to avoid. 🔹 Select: Provide focused context. Use file mentions or specific snippets; avoid overwhelming the AI. 🔹 Show: Reduce ambiguity with concrete examples – code samples, desired outputs, data formats, or mockups. 🔹 Simplify: Use clear, direct language. Break down complexity and avoid jargon. 🔹 Specify: Define exact requirements – expected outputs, constraints, data formats, edge cases. 🔹 Test: Plan your structure and features before prompting. Outline requirements like a PM/engineer. By applying these principles, you can significantly improve your collaboration with AI, leading to faster development cycles and better outcomes.

Working with AI Agents in production isn’t trivial if you’re regulated. Over the past year, we’ve developed five best practices: 1. Secure integration. Not “agent over the top” integration - While its obvious to most you’d never send sensitive bank or customer information directly to a model like ChatGPT often “AI Agents” are SaaS wrappers over LLMs - This opens them to new security vulnerabilities like prompt injection attacks - Instead AI Agents should be tightly contained within an existing, audited, 3rd party approved vendor platform and only have access to data within that 2. Standard Operating Procedures (SOPs) are the best training material - They provide a baseline for backtesting and evals - If an Agent is trained on and follows that procedure you can then baseline performance against human agents and the AI Agents over time 3. Using AI Agents to power first and second lines of defense - In the first line, Agents accelerate compliance officer’s reviews, reducing manual work - In the second line, they provide a consistent review of decisions and maintain a higher consistency than human reviewers (!) 4. Putting AI Agents in a glass box makes them observable - One worry financial institutions have is explainability, under SR 11-7 models have to be explainable - The solution is to ensure every data element accessed, every click, every thinking token is made available for audit, and rationale is always presented 5. Starting in co-pilot before moving to autopilot - In co-pilot mode an Agent does foundational data gathering and creates recommendations while humans are accountable for every individual decision  - Once an institution has confidence in that agents performance they can move to auto decisioning the lower-risk alerts.

Just read #OpenAI’s latest guide on building AI Agents. No fluff. No hype. Just clear, field-tested advice. Here are the 10 takeaways that really stayed with me — not just as a technologist, but as someone helping enterprises build agentic systems that last. 1. Start simple — with one #agent. It’s tempting to jump into multi-agent orchestration, but most use cases don’t need it upfront. In fact, multiple agents often introduce more chaos than value, especially when the basic workflow isn’t stable yet. 2. Choose your problems wisely. Agents shine where there's ambiguity — decision-making, exception handling, and unstructured data. If your task is predictable and rule-based, traditional automation will always be more efficient. 3. Start with the most powerful model. Establish your baseline with #GPT-4 or an equivalent. You need to prove the value first. Once it works, then fine-tune for speed and cost. 4. Your #SOPs are agent instructions waiting to happen. This one hit home. So much enterprise knowledge sits in playbooks and wikis — often ignored. Break them down into steps. Let the agent learn your process as it is, before redesigning it. 5. Tools need boundaries. Don’t make tools up as you go. Define clean interfaces — retrieval, execution, orchestration — and document them well. Reusable tools aren’t just efficient; they reduce technical debt. 6. Guardrails aren't optional. They're layered. There’s no single safety net. Combine prompt checks, rules, APIs, human feedback — whatever it takes to protect privacy, security, and intent. In high-trust environments, this matters more than anything. 7. Don’t over-engineer prompts. Use templates with variables. One solid base prompt that accepts policy or context inputs can scale across workflows. It’s easier to manage and debug. 8. Design for escalation from day one. What happens when an agent hits a blind spot? Or a high-risk situation? There must be a graceful, traceable way to hand off to a human — without friction. 9. Match orchestration to complexity. Some systems need a central ‘manager’ agent. Others are better off with distributed, peer-to-peer tasking. There’s no universal pattern — it’s about choosing what fits your use case. 10. Don’t wait for perfection — deploy early. Real users will always surprise you. The edge cases, the weird inputs, the unexpected outcomes — they show up only after you ship. Your best guardrails will be born from actual failures, not hypothetical ones. This isn’t theory. These are the kinds of lessons we apply every week as we build intelligent systems — where agents augment humans, not replace them. If you’re building in this space: 📌 Start small. 📌 Stay human-centric. 📌 Let trust scale with capability. Because building an agent is easy. Building a system you can trust — at scale, under pressure, and in the wild — is the real challenge. #AIagents #AgenticAI #LLMOps #EnterpriseAI #GauravWrites #BuildingWithTrust

Your AI agent sounds dumb because you haven't told it how to think. Most people build agents and hope for the best. Then wonder why it hallucinates, forgets context, or gives irrelevant answers. The truth? A poorly prompted agent will always underperform. A well-prompted agent becomes your best teammate. Here's exactly how to prompt an AI agent so it actually works: 📌 The 25 Agent Prompting Rules: 1. Define ONE job clearly – Not 20 tasks. One clear purpose. 2. List the exact tools it can use – Guardrails prevent chaos. 3. Teach it when to use each tool – Specific conditions, not guessing. 4. Set hard boundaries – What it MUST refuse, no exceptions. 5. Give personality only if necessary – Focus on function first. 6. Make it ask clarifying questions – Before it acts, it asks. 7. Force it to show reasoning – Explain the "why" before the "what." 8. Define escalation rules – When to ask a human for help. 9. Use edge case examples – Teach with real scenarios, not theory. 10. Specify exact output format – JSON, bullet points, tables—be precise. 11. Add a verification step – Check facts before responding. 12. Build in a hallucination check – "Did I make something up?" 13. Teach confirming questions – "Did I understand correctly?" 14. Set max response length – Forces clarity and focus. 15. Tell it to admit uncertainty – "I don't know" beats wrong answers. 16. Inject domain knowledge – Paste in your context/guidelines. 17. Add user handling rules – How to deal with frustrated users. 18. Define graceful "I don't know" – Better than guessing. 19. Specify tone & voice – Professional, friendly, casual—pick one. 20. Ask it to suggest next steps – Don't just solve, guide. 21. For customer service: Add brand voice – Keep consistency. 22. For sales agents: Define "qualified" – Who's a real lead? 23. For research: Require source verification – No made-up citations. 24. For code: Enforce quality standards – Clean, documented, tested. 25. Test worst-case scenarios first – Break it before users do. 📌 Why This Matters: A well-prompted agent handles 70-80% of work automatically. A badly prompted one wastes everyone's time. The difference? 30 minutes of thought upfront on your prompting strategy. Which of these 25 rules do you think your current AI agents are missing? Comment below, I'll share specific prompt templates for your use case. And if you're building agents, save this. You'll reference it constantly. ___________________________________________ 👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! Happy agenting! 🤖

I've reviewed 100s of AI agents, and one issue keeps popping up: Agents often lack context about the real world and their own internal logic. Two easy fixes can vastly improve how your agents perform: ☑ Always include real-world context in your prompts. Agents don't inherently understand the current time or location. Give them context explicitly: "current time," "user's location," or what's considered "recent" information. Example: If your agent uses a search tool, include timestamps so it knows what "recent" actually means. ☑ Make your internal logic transparent to the agent. Many developers embed critical business logic directly into their code, invisible to the agent. Clearly outline your logic within prompts so agents understand how information is processed and communicated. This transparency reduces friction and makes user interactions smoother and more effective. Great agents aren't just smarter—they're context-aware. Always remember these tiny improvements—they matter more than you think. I'm Shrey & I share daily guides on AI. If this helped you think about agents differently, click ♻️ reshare to help someone else build better agents.