Developing AI Agents

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. [Truncated for length. Full text: https://lnkd.in/gKDQ6H9s]

Satya Nadella described a future at Microsoft where there may be more than 20 million agents working alongside employees. This brings up interesting questions about how to monitor what these agents are doing, what these agents need to look like, and what they're allowed to access. Satya believes we need to start with the non-negotiables. Agents need to be fully inspectable and fully auditable. And the moment an agent can write code and execute it, that code has to run in an environment governed by policy. This is one of the engineering challenges of the AI moment; the infra that has to get built as companies stand up the platform for agentic work. If AI is going to amplify human capability at scale, we have to know what our agents are doing, constrain what they can access, and be able to audit and intervene when something goes wrong. It's what makes large-scale deployment possible. It's what earns trust. The alternative is launching millions of autonomous systems into production and hoping for the best.

Google published a free ~50 page whitepaper on the new SDLC with Vibe Coding & Agentic Engineering! Today I'm sharing a 50-page paper co-authored by me, Shubham Saboo and Dr. Sokratis Kartakis Kartakis, and part of Google's 5-day AI Agents course on Kaggle. It's free and we think you'll find it a useful read. AI compresses implementation from weeks to hours. But requirements, architecture, and verification stay stubbornly human-paced. That asymmetry changes everything. The bottleneck isn't typing anymore. It's spec quality. Vibe coding and agentic engineering aren't different tools. They're different disciplines. The difference isn't whether you use AI. It's how much structure, verification, and human judgment surrounds the output. Casual prompts and accepted-whatever-came-back is vibe coding. Formal specs, automated eval suites, CI gates, and human oversight of architecture is agentic engineering. Both use the same agent. What separates them is the harness. Agent = Model + Harness. There's a temptation to treat model quality as the explanation for everything good and bad about your agent. It's wrong, and it leads to the wrong investments. The model is the engine. The harness - the prompts, tools, rule files, sandboxes, guardrails, orchestration logic, observability - is the car, the road, and the traffic laws. When an agent does something wrong, the first instinct is to blame the model. More often the failure traces back to a missing tool, a vague rule, an absent guardrail, or a context window stuffed with noise. Most agent failures, examined honestly, are configuration failures. Three things I believe will stay true as the tools change: Structure scales, vibes don't. Vibe coding is valid for exploration and prototypes. For software organizations depend on, the discipline of agentic engineering is not optional. AI amplifies your engineering culture. Strong testing practices and clear architectural standards get dramatically more value from AI than teams without them. It's a force multiplier, and it multiplies both your strengths and your weaknesses. The human role is evolving, not diminishing. The builders who understand architecture, define precise specifications, and evaluate output critically are more valuable than ever. The skills that matter are shifting from implementation to judgment - from writing code to designing the systems that produce code. Generation is solved. Verification, judgment, and direction are the new craft. We hope you find the new whitepaper a helpful read! Download the PDF here: https://lnkd.in/gPsGzjPZ #ai #programming #softwareengineering

Agent memory has quickly become one of the most discussed topics in AI. As more teams start building real agent systems, one limitation keeps showing up: agents don’t remember much. Most agents today are essentially stateless. They can reason through tasks, call tools, and generate impressive responses, but once the session ends, the system forgets everything. And the conversation often gets simplified into two layers: 𝟏) 𝐒𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 = 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐰𝐢𝐧𝐝𝐨𝐰 This is where agents keep conversation history, reasoning steps, and recent tool outputs.  But context windows are temporary. 𝟐) 𝐓𝐡𝐞𝐧 𝐭𝐡𝐞𝐫𝐞’𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐦𝐨𝐫𝐲 This is where agents remember things across sessions: - user preferences - past interactions - knowledge collected over time - intermediate results from previous tasks And once you start thinking about long-term memory, the problem quickly shifts. This is really a data infrastructure problem. In many cases, long-term agent memory ends up living in the data layer. Which is why databases are starting to play a bigger role in modern AI systems. For example, 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 has been building more capabilities around this idea. By integrating vector search directly into the database, application data and embeddings can live in the same system instead of being split across multiple tools. That makes it easier to store agent memory, retrieve relevant context with vector search, and keep embeddings synchronized with the underlying data. For teams building AI systems, this kind of architecture reduces a lot of the complexity around memory. If you're exploring this space, MongoDB’s Learning Hub has some useful courses and hands-on labs worth checking out https://lnkd.in/gazxytWP As agents start running longer workflows, memory quickly becomes part of the system architecture. Designing how that memory is stored, retrieved, and updated may turn out to be one of the most important pieces of agent design. #aiagents #agenticai #machinelearning #data #database

AI security/securing the use of AI is going to kill me. I use Claude Code almost daily. It's a problem.... Here's what I have to change AGAIN this week. Security researcher Ari Marzuk disclosed 30+ vulnerabilities across AI coding tools. Cursor. GitHub Copilot. Windsurf. Claude Code. All of them. He called it IDEsaster. The attack chain includes prompt injection, hijacking LLM context, and auto-approved tool calls executing without permission. Then, legitimate IDE features are weaponized for data exfiltration and RCE. Your .env files. Your API keys. Your source code. Accessible through features you thought were safe. Most studies I read claim that around 85% of developers now use AI coding tools daily. Most have no idea their IDE treats its own features as inherently trusted. 𝗦𝗼... 𝗮𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗿𝗶'𝘀 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗵𝗲𝗿𝗲'𝘀 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗶𝗻𝗴... Be warned: All this is SO much easier said than done! Audit every MCP server connection. Checked for tool poisoning vectors where legitimate tools might parse attacker-controlled input from GitHub PRs or web content. Removed servers I couldn't verify. Disabled auto-approve for file writes. The attack chains weaponize configuration files and project instructions like .claude/settings.json and CLAUDE.md. One malicious write to these files can alter agent behavior or achieve code execution without additional user interaction. Move all credentials to a secrets manager. No .gitignored .env files in agent-accessible directories. API keys live in 1Password CLI. Environment variables inject at runtime through a wrapper script the LLM never sees. Start running Claude Code in isolated containers. Mounted volumes limited to specific project directories. No access to ~/.ssh, ~/.aws, or ~/.config. If the agent gets compromised, blast radius stays contained. Enable all security warnings. Claude Code added explicit warnings for JSON schema exfiltration and settings file modifications. These exist because Anthropic knows the attack surface. Add pre-commit hooks for hidden characters. Prompt injections hide in pasted URLs, READMEs, and file names using invisible Unicode. Flag non-ASCII characters in any file the agent might ingest. The fix isn't to stop using AI coding tools. The fix is to stop trusting them implicitly. What controls do you have for AI tools with write access to your codebase? 👉 Follow for more AI and cybersecurity insights with the occasional rant #AISecurity #DevSecOps

If you're feeling overwhelmed with how fast AI is evolving, you're not alone. Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind. But the good news is that you don’t need to learn everything all at once. What you need is structure. So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable. 💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition. If you're moving faster or slower based on time or experience, that’s okay too. And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here. 👇 Here’s the roadmap: 🔖 Level 1: GenAI & Transformer Foundations Tokens, embeddings, transformers, decoding, and inference with open-weight models. 🔖 Level 2: Prompting & Language Model Behavior Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting. 🔖 Level 3: Retrieval-Augmented Generation (RAG) Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation. 🔖 Level 4: LLMOps & Tools LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data. 🔖 Level 5: Agents & Agent Frameworks Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation. 🔖 Level 6: Memory, State & Orchestration Vector and symbolic memory, episodic vs persistent state, memory compression. 🔖 Level 7: Multi-Agent Systems Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams. 🔖 Level 8: Evaluation & Reinforcement Learning LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops. 🔖 Level 9: Protocols & Safety MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates. 🔖 Level 10: Build & Deploy FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens. 📌 Bookmark this. 🛠️ Build something after every level. And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 💻 Pro-tip: Start with cookbooks! 〰️〰️〰️ Follow me (Aishwarya Srinivasan for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Just a year ago it was all about GenAI. Today the spotlight is on Agentic AI. What is driving the shift? 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 -  GenAI: Models that create or transform content in response to prompts. - Agentic AI: Systems that can pursue goals, plan tasks, and take action with minimal human input. GenAI helps generate ideas; Agentic AI takes action and gets things done. 𝗚𝗲𝗻𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 - Rapid content & pattern creation - Natural‑language front ends for analytics Finance examples • Auto‑drafted research and client letters • Multilingual regulatory summaries • Synthetic stress‑test narratives 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • Real‑time decisioning under uncertainty • Multi‑skill chaining (retrieve → reason → act) • Continuous learning from outcomes Finance examples • Millisecond fraud-blocking on millions of card transactions • Dynamic risk-rule tuning based on issuer feedback • Automated receivables follow-up and payment posting • Autonomous treasury operations: FX hedging and overnight liquidity management 𝗪𝗵𝘆 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 Moving from GenAI to Agentic AI fundamentally changes how financial services deliver value: • Real-time revenue protection: agentic systems can reroute or block high-risk payments instantly, slashing fraud losses and chargebacks. • Seamless customer journeys: fully automated KYC and onboarding flows. • Dynamic liquidity management: treasury bots rebalance cash, execute FX hedges, and optimize funding costs overnight. • End-to-end payment orchestration from choosing the most cost-effective cross-border rail to retrying failed pay-outs. • Regulatory agility: continuous-monitoring agents track rule changes, update compliance workflows, and generate audit trails without manual intervention. 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁 • Conversational banking agents: go beyond answering questions - initiate transfers, set up recurring payments, and negotiate loan terms. • Embedded Finance at scale: agents orchestrate lending, insurance, and FX in real time within non-financial apps. • On-demand cross-border settlement: smart agents choose between CBDCs, stablecoins, or traditional rails to settle payments instantly at the lowest cost. • Predictive risk & credit scoring: continuously update merchant and counterparty scores as new data streams in. • Auto-remediating systems: agents detect and fix platform issues in real time - no human ops required. • Automated regtech: agentic workflows handle licensing, screening, reporting, and audits - cutting compliance time from weeks to hours. • AI treasury market making: bots quote and underwrite liquidity in real time, adjusting spreads dynamically to market shifts. Opinions: my own 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

We’re witnessing a shift from static models to 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗵𝗶𝗻𝗸, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁—not just respond. But with so many disciplines converging—LLMs, orchestration, memory, planning—how do you 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 to master it all? Here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 to navigate the Agentic AI landscape, designed for developers and builders who want to go beyond surface-level hype: ↳ 𝟭. 𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Move from model outputs to goal-driven autonomy. Understand where Agentic AI fits in the automation stack. ↳ 𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Before agents, there’s learning—deep learning, reinforcement learning, and the theories powering adaptive behavior. ↳ 𝟯. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Dive into 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜—frameworks enabling coordination, planning, and tool use. ↳ 𝟰. 𝗚𝗼 𝗗𝗲𝗲𝗽 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: Learn how tokenization, embeddings, and memory management drive better reasoning. ↳𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Agents aren’t lone wolves—they negotiate, delegate, and synchronize in distributed workflows. ↳𝟲. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Understand how 𝗥𝗔𝗚, vector stores, and semantic indexing turn short-term chatbots into long-term thinkers. ↳𝟳. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗦𝗸𝗶𝗹𝗹: Build agents with layered planning, feedback loops, and reinforcement-based self-improvement. ↳𝟴. 𝗠𝗮𝗸𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗗𝘆𝗻𝗮𝗺𝗶𝗰: From few-shot to chain-of-thought, prompt engineering is the new compiler—learn to wield it with intention. ↳𝟵. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 + 𝗦𝗲𝗹𝗳-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Agents that improve themselves aren’t science fiction—they're built on adaptive loops and human feedback. ↳𝟭𝟬. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Master hybrid search and scalable retrieval pipelines for real-time, context-rich AI. ↳𝟭𝟭. 𝗧𝗵𝗶𝗻𝗸 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗗𝗲𝗺𝗼𝘀: Production-ready agents need low latency, monitoring, and integration into business workflows. 𝟭𝟮. 𝗔𝗽𝗽𝗹𝘆 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: From copilots to autonomous research assistants—Agentic AI is already solving real problems in the wild. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗼𝘂𝘁𝗽𝘂𝘁𝘀—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹, 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. If you're serious about building the next wave of intelligent systems, this roadmap is your compass. Curious—what part of this roadmap are you diving into right now?

I tried EVERY major AI Coding tool so you don’t have to. Here’s what I learned about each one - and which one’s the best for your particular use case 👇 After an entire weekend of hands-on testing 15+ AI coding assistants, building the same real-life application (tax comparison calculator), and documenting every step - here's the comprehensive breakdown to separate the signal from the noise: 🏆 Best Overall: Cline - 100% open source and free version of Cursor + Windsurf that’s a simple VS Code extension - Truly thoughtful agentic coding with extensive tool use (terminal, computer use, websites, etc) - Wrote the best code with fewer mistakes, better self-healing, but no inline chat 🎨 Best for Non-Technical Users: Vercel V0 - Fast, Easy, intuitive UX - Strong community and templates - Component-specific editing via AI is magical ⚡Best for Quick Prototypes: Anthropic Claude 3.5 Sonnet - Fast & clean responses - Great reasoning & logic clarity - Artifact is great for prototyping, with ability to publish and share Replit: Good for full-stack cloud development, but sits in an awkward spot—too complex for beginners, too constrained for advanced users. StackBlitz Bolt.new: A standard cloud IDE with AI codegen, but nothing special. Lovable: Similar to Bolt, but unreliable AI-generated code, hard to toggle/see code. Cursor: Great Copilot alternative, but lacks extensive agentic capabilities like Cline. Codeium Windsurf: Strong agent mode but agent was sometimes lazy and incomplete. GitHub Copilot: Good for simple inline edits, but lacks full agentic workflow (though an agent mode was recently released). Aider: Terminal & keyboard only. Feels like Vim/Emacs on steroids. Too hardcore. OpenHands: Open-source and free Cognition Devin with strong agentic coding, but SaaS version is unstable. OpenAI (o3-mini-high): Good logic depth but lacks a coding canvas. Anthropic (Claude 3.5 Sonnet): Fast + clean. Artifact is great for prototypes, but can’t edit code directly inside it. Google Gemini 2: Poor experience—lazy, incomplete code. Generated separate files that I had to manually combine. DeepSeek AI R1: Strong long reasoning chains, but gets a lot of logic wrong. Tempo (YC S23): Promising PRD → Design → Code → Deploy workflow, but still in early stages. Onlook: Strong for design-first workflows but inconvenient for direct code editing. Reweb: Generates only UI components, not code with logic. My Final Recommendations: - For non-technical users: Vercel V0 is the best no-code/low-code option. - For cloud-based development: Try Bolt. - For local AI-powered coding: Cline is free and outperforms Cursor/Codeium. - For rapid prototyping: Claude 3.5 Sonnet is fast and effective. - For designers: Tempo or Onlook provide a strong UI-first workflow. Do you want to see a full write up of my AI coding experiences? Let me know if I should make a full post comparing AI Coding tools in detail by sharing this post and commenting below.

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!