Best Practices for Managing AI Project Context

Obsessing over the perfect prompt only takes you so far. The key to building AI agents that actually work in production? 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Why it matters: - LLMs don’t have infinite attention. - Every extra token eats into a finite “attention budget.” - At some point, too much context causes “context rot”, where the model forgets or confuses the very thing you wanted it to recall. Anthropic 𝘀𝗵𝗮𝗿𝗲𝗱 𝘀𝗼𝗺𝗲 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 1/ Start with a minimal but clear, structured system prompt. 2/ Provide few, well-designed tools that are token-efficient & unambiguous. 3/ Use a few canonical examples, not exhaustive lists of edge cases. 4/ Use “just-in-time” context (loading only what’s needed dynamically) instead of frontloading all data. 5/ Summarize and refresh context or persist memory outside the model's window for long-horizon tasks and continuity. As models improve, they need less handholding. But context engineering will remain essential. It ensures agents stay coherent, efficient, and effective, especially in long, complex tasks. For more info, check out the original report from Anthropic in comments ↓ #EnterpriseAI #AIAgents #AIforBusiness

𝐖𝐡𝐲 𝐃𝐨 𝐌𝐨𝐬𝐭 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐅𝐚𝐢𝐥 𝐃𝐞𝐬𝐩𝐢𝐭𝐞 𝐔𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐞𝐬𝐭 𝐌𝐨𝐝𝐞𝐥𝐬? Not because the model is weak.  Because the context is broken. Prompting is just one slice.  The real craft is context engineering deciding what the model sees, when it sees it, and how information flows through every turn. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝟔 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐞𝐯𝐞𝐫𝐲 𝐚𝐠𝐞𝐧𝐭 𝐧𝐞𝐞𝐝𝐬? 𝟏. 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧𝐬 • Defines the mission, rules, and expected behavior. • Includes role (who the agent acts as), objective (why it matters), steps (reasoning and actions), and requirements (how to respond). • This sets the direction get it wrong and nothing downstream recovers. 𝟐. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 • Shows the agent what good and bad looks like. • Positive examples to imitate. Negative examples to avoid. • Few-shot examples often outperform longer instructions. Three great examples beat three paragraphs of rules. 𝟑. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 • Facts and information the agent should know. • External context, workflow processes, task documents, structured data. • This is the foundation of every RAG system retrieve knowledge, add to context, use in reasoning. 𝟒. 𝐌𝐞𝐦𝐨𝐫𝐲 • What the agent remembers across interactions. • Short-term: recent messages, chat history, reasoning state. • Long-term: user preferences, past interactions, learnings. • This is the difference between a chatbot and an assistant. Without memory, every conversation starts from zero. 𝟓. 𝐓𝐨𝐨𝐥𝐬 • Actions the agent can take to get things done. • Each tool needs a clear description, input schema, when to use it, and example usage. • Without tools, your agent is just a talker. 𝟔. 𝐓𝐨𝐨𝐥 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 • The outcome returned after the agent takes action. • Raw output, success or failure status, data logs, errors. • This closes the loop and is where most agent loops silently break. If the agent can't interpret tool results properly, it hallucinates the next step. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐝𝐞𝐛𝐮𝐠 𝐚 𝐛𝐫𝐨𝐤𝐞𝐧 𝐚𝐠𝐞𝐧𝐭? Agent hallucinating? Check knowledge and memory. Ignoring instructions? Check the instruction layer. Looping endlessly? Check tool results the loop isn't closing. Wrong tone or format? Check examples. Not taking action? Check tool definitions. The answer almost always lives in one of these six layers, not the model. Master context engineering and you stop fighting your agent.  You start designing it. Which context layer is your agent weakest at today? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: Found this useful? Join 2,500+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. I break down real enterprise AI systems, agentic patterns, and what actually works in production. ✉️ Free subscription: https://lnkd.in/exc4upeq #ContextEngineering #AIAgents #GenAI

Have you heard of 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗔𝘀𝘀𝗲𝗺𝗯𝗹𝘆 (𝗗𝗖𝗔) in 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴? It is the practice of constructing the model’s input ON DEMAND from multiple context sources—based on the user goal, current task state, tool outputs, risk level, and token budget. Static prompts assume the world is stable. Real systems aren’t, especially in Agentic AI.  1. Users change their mind mid-flight  2. Tools return surprises  3. Policies differ by tenant/workflow  4. Long-horizon tasks need stepwise context, not one giant dump DCA turns context into a living artifact that evolves across turns and phases. Think of your context as a bundle with explicit compartments: 1. 𝗧𝗮𝘀𝗸 𝗙𝗿𝗮𝗺𝗲 ( Goal, scope, constraints, definition of done, what’s in/out, timeline, rules ) 2. 𝗚𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 ( Retrieved docs, structured records, citations, tool outputs, ranked, deduped, freshness-aware ) 3. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝗮𝘁𝗲 ( Plan, progress, decisions made, open questions, scratch summaries, checkpoints, rollback points ) 4. 𝗣𝗼𝗹𝗶𝗰𝘆 + 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 ( Safety rules, compliance requirements, PII handling, Tenant policies, workflow-specific rules ) 5. 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 ( System/developer instructions, style, format contracts, schemas, validators ) Dynamic context assembly is the orchestrator that decides what to include, in which step, how much and in what order 𝗗𝗖𝗔 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 1. Context is a contract where each department has a purpose & budget 2. Prefer structured snippets over raw text 3. Always include provenance ( source, timestamp, confidence) for grounding content 4. Separate evidence from instructions 5. Checkpoint summaries every N steps, so state does not rot over long horizons 6. Make trimming deterministic. 7. Treat tool outputs as first-class context, but sanitize and normalize them. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗮𝗻𝘁𝗶-𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 1. Shoving more docs in without reason 2. Mixing different compartments into one single blob 3. Not managing agent state 4. No freshness / authority scoring for grounding truth #ContextEngineering #AIAgents #RAG #LLMOps #GenAIOps #AgenticAI #EnterpriseAI

Context Engineering is the #1 Gen AI Skill in 2026 For 99% people, that means using 'Projects' properly LLMs like ChatGPT and Claude have this powerful feature called Projects However, most NEVER set up project correctly - leading to more AI slop First thing, remember every project has 3 components ↳ Memory ↳ Instructions ↳ Files 1. Separate Instructions from Knowledge (when setting up Project) ↳ Instructions: HOW to behave (tone, format) ↳ Files: WHAT to know (uploaded files) Example: Upload 'Style Guide' or 'Design System' as a file in Files, not as an Instruction 2. Be Explicit in Instructions ↳ Don't write vague instructions like 'Be helpful and organized' ↳ Use explicit structure such as 'Problem - Why It Matters - Solution - Example' In my usage and testing, I have found that Claude 4.x and GPT-5.x follow instructions quite literally 3. Upload Files, Don't Paste in conversations ↳ Stop copy-pasting brand guidelines into every chat ↳ Upload once: PDFs, Docs, spreadsheets, images and AI references automatically ↳ Then reference them, instead of re-explaining them every time 4. Use Markdown Structure (get help from ChatGPT) ↳ Headers, bullets, code blocks beat an unformatted blob of text ↳ Models read structured formatting more reliably 5. Name Files Descriptively ↳ Don't upload files with names like 'Document1.pdf' ↳ Name it appropriately such as 'Brand_Voice_Guidelines_2026.pdf' ↳ Filenames help AI retrieve the right context 6. Keep Knowledge Base Lean ↳ More files ≠ better performance. ↳ Too much context leads to performance degradation ↳ Only upload what's essential for THIS project 7. Isolate by Context Boundary ↳ Treat each project as a separate memory bubble ↳ Client A work stays separate from Client B ↳ Confidential work should not overlap into client-facing projects 8. Add Examples for Complex Tasks (few-shot) ↳ Simple tasks: skip examples ↳ Specific workflows: show 1-2 good quality sample exchanges 9. Audit Monthly ↳ Memory: ChatGPT default: viewable/editable overall for user ChatGPT project-only: BLACK BOX Claude: transparent, editable summaries for each Project ↳ Files: Delete outdated docs, old team members, expired info Stale context leads to degraded performance I, myself, break down projects like this: ↳ one Project per client ↳ one for PM work - experiments, prototypes ↳ one for Strategy ↳ one for LinkedIn posts ↳ one for the newsletter No matter what work you do - whether it is strategy, writing, research, media, content, documentation - your LLM is only as good as the context you give it. --------- I am Priyadeep Sinha and I help you level up with AI one strategy at a time For my best, most detailed resources, subscribe to my weekly newsletter Work in Beta where I share the AI strategies I used as a VP-Product building winning AI products: https://lnkd.in/gPqYEzaJ

When your AI sounds clever but spits nonsense, it’s not hallucinating. It’s missing the right context. Most prompts throw in random or outdated info. That’s like giving a chef spoiled ingredients and expecting a gourmet meal. Context isn’t just decoration anymore. It’s the whole recipe. Today’s best AI setups don’t just dump data in. They carefully decide what to pull, when, and how to keep the context clean. Here’s what a well-engineered context looks like: • Working memory: recent interactions and tool outputs needed right now   • Long-term memory: user habits, past results, and facts stored smartly for later   • Dynamic retrieval: smart query tweaks, ranking, and trimming before feeding the model   • Tools as partners: APIs, search engines, and servers called only when needed  Example: A coding AI keeps track of recent errors and code changes in working memory. It stores project files and dependencies in long-term memory. When stuck, it fetches docs or runs web searches. The result? Faster, more reliable code without nonsense. If you’re building AI agents, focus on: • Sharpening retrieval: rewrite queries, rerank results, compress context before the model sees it   • Splitting memories: keep short-term fresh and long-term lean with only key facts   • Using tools like sensors: call them when info is missing, don’t expect the model to guess   • Defining clear context rules: schemas for tools and outputs that the system follows  Your current retrieval-augmented setup isn’t useless. It’s the base. The trick now is orchestration — giving the model just the right slice of context to do its job well. So next time your AI messes up, don’t tweak the prompt first. Fix the context. Then watch it finally get it right. I’m Shrey Shah & I share daily guides on AI. If this helped, hit the ♻️ reshare button to help someone else build smarter AI.

I asked Claude to audit its own project context. It told me to delete half my workspace. I've been building something over a few weeks. Each session, I save a .md summary into the project so the next session has context. Standard practice. After today's session, I asked Claude to audit the workspace. It came back categorized: clear deletes, ambiguous calls, and keepers. The ambiguous calls looked like this: ➖ Drafts I wasn't sure had shipped.  ➖ Context that could be one-offs or templates.  ➖ Project files I had to label pre-pivot or post-pivot. Some of the "clear deletes" were recent. But the problem framing in them had been superseded two days earlier, and keeping them would risk future sessions reverting to the outdated design. The same pattern shows up anywhere AI builds compound: system prompts, RAG corpora, agent memory, knowledge bases, etc. 𝐂𝐚𝐥𝐥 𝐢𝐭 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐜𝐮𝐫𝐚𝐭𝐢𝐨𝐧. Three practices I'm adopting: ➖ Audit context before major working sessions. Especially after pivots, architecture changes, or prompt rewrites. ➖ Write context for a stranger. If a note only makes sense because you remember the backstory, it's not context. ➖ Treat context like code. Add version control, timestamps, and 'superseded by' markers. Future-you (and future AI) will thank you. The model can't tell which version of your thinking is current. It averages across them. Stale context is worse than no context. Curate yours. #AI #ContextEngineering #ContextCuration

Everyone is focused on Anthropic's learning hub. When the real edge for AI engineers is in their engineering blog It’s a masterclass in how the teams behind Claude actually build, test, and scale their models. A compilation of production-grade wisdom. Here’s a look at what you can find on this engineering arc: • 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: A deep dive into their orchestrator-worker architecture and how they use prompt engineering for complex coordination. • 𝗠𝗼𝗱𝗲𝗹 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 & 𝗦𝗮𝗳𝗲𝘁𝘆: "Inverse Scaling" (why more compute isn't always better), "Subliminal Learning" (how models pick up hidden traits), and how they counter misuse. • 𝗠𝗼𝗱𝗲𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: Technical breakdowns on concepts like "Persona Vectors" to understand and steer model personality. • 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Actionable playbooks on turning models into reliable, goal-driven agents. Their recent post on context engineering is a godsend for anyone building serious AI agents. It’s one of the most practical breakdowns I’ve read on moving beyond simple "prompt engineering" to true 𝘴𝘺𝘴𝘵𝘦𝘮-𝘭𝘦𝘷𝘦𝘭 context management. Here’s the breakdown: • 𝗜𝘁’𝘀 𝗮𝗻 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗕𝘂𝗱𝗴𝗲𝘁 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: ↳ LLMs have a finite "attention budget." ↳ Overload them with low-signal information, and you get "context rot." ↳ The goal is to provide the smallest, highest-signal set of tokens. • 𝗨𝘀𝗲 "𝗝𝘂𝘀𝘁-𝗶𝗻-𝗧𝗶𝗺𝗲" 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: ↳ Don't stuff the context window. ↳ Use lightweight identifiers (like file paths or URLs) and let the agent use tools to pull in the full data only when needed. • 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗡𝗼𝘁𝗲-𝗧𝗮𝗸𝗶𝗻𝗴: ↳ For long tasks, give the agent a <𝚜𝚌𝚛𝚊𝚝𝚌𝚑𝚙𝚊𝚍> to write down its thoughts, plans, and intermediate results. ↳ This acts as an external memory and keeps it on track. • 𝗖𝗼𝗺𝗽𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗞𝗲𝘆: ↳ For long conversations, don't pass the entire chat history. ↳ Use an agent to summarize previous turns, preserving key information while saving tokens. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗦𝘂𝗯-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀: ↳ Decompose complex problems. ↳ Have a main "orchestrator" agent that delegates sub-tasks to specialized agents, each with its own focused, clean context window. Find the full engineering arc here: https://lnkd.in/gZtZ-nuu And the context engineering deep dive is a must-read: https://lnkd.in/gvy8vpxt What are you reading first? 👇 ♻️ Repost to help your network upskill.

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

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!

Building smarter AI systems requires more than just better models — it requires better management of context. That’s where the Model Context Protocol (MCP) comes in. → MCP separates what the AI needs to know (context) from how the AI thinks (the model itself). → It helps different clients, servers, and language models communicate in a standardized way. → It keeps track of memory, tokens, user history, and tools — so AI agents can act with full awareness. This whiteboard sketch shows the big idea: → MCP Server manages the context and coordinates between users, AI agents, and different models. → MCP Client talks to the server and sends requests (with full context) to models like OpenAI, Claude, and others. → Tools like GitHub, Slack, Snowflake can easily connect into this system. → Modern agent frameworks like LangChain, LangGraph, and LlamaIndex are fully supported. Why it matters: → You don’t need to rebuild everything for each model. → You don’t lose important context across conversations. → You can scale AI across teams, projects, and tools — faster and more reliably. MCP makes complex AI systems modular, flexible, and production-ready.