RajaCSP

Agentic System Design Concepts - Patterns Every AI Engineer Should Know

2026-04-11T00:00:00-03:00

Building reliable AI agents isn't just about picking the right model — it's about the patterns you wire around it. Here's a concise reference of 15 agentic system design concepts worth knowing. Two lines each — just enough to understand what they do and why they matter.

Resilience & Failure Isolation

Agent Circuit Breaker — Prevents cascading failures by halting agent execution when downstream services or tools are repeatedly failing. Borrowed from distributed systems engineering, it stops a single broken tool from dragging the entire agent pipeline down.

Blast Radius Limiter — Restricts the impact of an agent failure to a defined scope so it can't propagate across the system. Think of it as a blast door: when something goes wrong, the damage stays local.

Dead Letter Queue for Agents — A holding area where failed or unprocessable agent tasks are parked for later inspection instead of silently dropped. It gives you a recoverable audit trail when tasks fall through the cracks at runtime.

Control Flow & Decision Quality

Orchestrator vs Choreography — Defines whether agent interactions are centrally directed (orchestrator controls all moves) or emergent (agents react to events and coordinate peer-to-peer). The choice shapes coupling, debuggability, and how gracefully the system degrades.

Confidence Threshold Gate — Ensures an agent only takes action when its internal confidence in a decision clears a defined threshold. A simple but powerful reliability lever: low-confidence branches pause for human review rather than guessing forward.

Replanning Loop — Allows agents to re-evaluate their plan mid-execution when context changes or a step fails, rather than continuing blindly on a stale plan. Essential for long-horizon tasks where the environment isn't static.

Human Escalation Protocol — Provides a structured mechanism for agents to hand off to a human when they're stuck, uncertain, or handling high-stakes decisions. It's not a failure mode — it's a designed off-ramp.

Tool Invocation Reliability

Idempotent Tool Calls — Ensures that a tool can be called multiple times with the same inputs without producing unintended side effects. Critical in agentic pipelines where retries happen frequently due to timeouts or partial failures.

Tool Invocation Timeout — Prevents agents from blocking indefinitely on a tool that is slow or unresponsive, forcing a graceful fallback or retry. Without this, a single flaky API can freeze an entire agent run.

Context Window Checkpointing — Periodically saves the agent's progress so it can resume from a known-good state rather than restarting from scratch after a context overflow or crash. Especially important for long-running, multi-step tasks.

Infrastructure & Routing

LLM Gateway Pattern — A single abstraction layer that manages all LLM API calls, handling routing, rate limiting, retries, and observability in one place. It decouples agent logic from model-specific SDKs, making provider swaps painless.

Semantic Caching — Stores LLM responses keyed on semantic meaning rather than exact input strings, so similar queries hit the cache even when phrased differently. Reduces latency and cost without sacrificing answer quality.

Multi-Agent State Sync — Maintains a consistent shared state across multiple agents working in parallel or in sequence. Without it, agents operating on stale or divergent state produce contradictory or redundant outputs.

Observability & Deployment

Agentic Observability Tracing — Tracks every decision, tool call, handoff, and LLM interaction across an agent run, producing a full execution trace for debugging and performance analysis. The difference between guessing why something failed and knowing.

Canary Agent Deployment — Rolls out a new agent version to a small slice of production traffic before full release, allowing you to compare behavior and catch regressions with limited blast radius. Applies standard software deployment discipline to the agent layer.

Every Claude Code Concept You Need to Know

2026-04-11T00:00:00-03:00

Claude Code is not a chatbot. It lives in your terminal, reads your actual files, writes code, runs commands, and executes multi-step workflows — all with your permission. Here are 30 concepts you need to understand it properly. No fluff, no hand-holding.

The 30 Concepts

1. The Terminal — Claude Code doesn't run in a browser. It runs in the terminal, the same text-based interface developers use daily. If you've never opened a terminal before, that's your first homework assignment.

2. Installation + Pricing — Install with a single command via npm. Pricing is token-based through your Anthropic account. There's no flat monthly fee tied to a UI — you pay for what you use, which means costs scale with how hard you push it.

3. File Access — Claude Code reads and edits files directly on your machine, with your permission. Not "paste your doc into a chat window." It opens the actual file, modifies it in-place, and saves it. This is the concept that makes it useful.

4. Image + PDF Reading — Claude Code can ingest images and PDFs as inputs. Point it at a PDF proposal or a screenshot and it processes the content directly — no manual copy-paste required.

5. Tool Use — Claude Code has built-in tools: file reading, file writing, shell execution, and more. These are the primitives it uses to act on your computer. You see each tool call as it happens in real time.

6. Prompting Techniques — Vague prompts produce garbage results. "Help me with my marketing" is useless. "Write a 3-email welcome sequence for my dog walking business targeting first-time pet owners, 150 words each" is not. Specificity is the skill.

7. CLAUDE.md — A markdown file you create in your project directory that tells Claude Code the rules, context, and conventions for that project. Think of it as a standing system prompt that persists across sessions. Every serious Claude Code user has one.

8. Plan Mode — Before Claude Code executes anything, you can ask it to plan first. It outputs what it intends to do, step by step, and waits for your approval. Run in plan mode for anything non-trivial. Review before you let it touch anything.

9. Context Window — The amount of text Claude can "hold in mind" at once during a session. Long conversations, large files, and extensive histories eat into it. When context fills up, older information gets dropped. This affects result quality.

10. Tokens + Costs — Everything processed by Claude Code — your prompts, the files it reads, its responses — is measured in tokens. Tokens drive cost. Reading a 50-page PDF burns tokens. Keep context lean and targeted to control spend.

11. Model Selection — You can choose which Claude model backs your session. Faster, cheaper models work for routine tasks. Heavier models are worth it for complex reasoning or production-grade code. Pick the right tool for the job.

12. /compact — A slash command that compresses your current conversation history into a shorter summary, freeing up context window space without wiping the session. Use it mid-task when context gets bloated.

13. /clear — Wipes the entire conversation and starts fresh. Every new task should start with a clean context. Don't carry leftover noise from a previous task into the next one. Use this more than you think you need to.

14. Session Management — Claude Code has no persistent memory between sessions by default. Start each session with your CLAUDE.md re-read to restore project context. Design your workflow around this statelessness rather than fighting it.

15. Permission Modes — By default, Claude Code asks for approval before running any shell command. This gets tedious fast. You can pre-approve safe, non-destructive commands (ls, cat, grep, mkdir, git status) in your settings.local.json. Destructive operations should always require explicit confirmation.

16. Effort Levels — You can signal how much effort you want Claude to apply. Quick answers for exploration, thorough analysis for production decisions. Matching effort level to task type saves time and tokens.

17. Interrupt + Redirect — While Claude Code is running a task, you can interrupt it mid-execution and redirect it. If it starts going down the wrong path, stop it early. Don't let it burn tokens on a wrong approach when you can see it happening.

18. Visual Studio Code — Claude Code integrates directly with VS Code. You can run it inside the VS Code terminal and see file changes reflected in your editor in real time. If you're not a terminal-native developer, this is the recommended setup.

19. Memory — Claude Code supports memory files that persist across sessions. Unlike CLAUDE.md (project-specific), memory files can store user-level preferences and context. Useful for encoding your personal conventions once and never repeating them.

20. Project vs Global — Configuration can be scoped at the project level (CLAUDE.md, settings.local.json) or at the global level (applies to all Claude Code sessions on your machine). Know which scope a setting lives in before you modify it.

21. Slash Commands — Built-in commands prefixed with / that control Claude Code's behavior: /clear, /compact, /help, and more. You can also define custom slash commands (skills) that map to your own workflows.

22. Skills — Custom slash commands you define once and reuse indefinitely. A skill is a markdown file that describes a reusable workflow. You build it once, invoke it with /skill-name, and Claude follows the instructions every time. Hundreds of community-built skills already exist on GitHub in repos like anthropics/skills and hesreallyhim/awesome-claude-code.

23. Hooks — Scripts that run automatically before or after Claude Code actions. Quality gate hooks, for example, can intercept Claude's output before it's committed and check it against defined standards. Hooks are how you enforce consistency without relying on Claude to self-police.

24. Web Browsing — Claude Code can browse the web when given the appropriate tool access. It can fetch pages, read documentation, and pull in live information as part of a task — not just work from static local files.

25. MCP Servers — Model Context Protocol servers extend Claude Code's tool access to external services: Airtable, Google Drive, Slack, GitHub, and more. Tools handle what Claude does on your computer. MCP extends that to the internet and third-party APIs. This is the integration layer.

26. Perplexity MCP — A specific MCP integration that gives Claude Code access to Perplexity's search capabilities. Useful when a task requires real-time research as part of a larger automated workflow.

27. Subagents — Multiple Claude Code instances running simultaneously, each handling a distinct subtask. Instead of processing platforms one at a time, you spin up parallel agents and run them concurrently. Subagents are how you turn Claude Code from a sequential tool into a parallel workflow engine.

28. Remote Control — Claude Code can be configured for remote access, meaning you can trigger and manage sessions from another machine or interface. Relevant for server automation and scheduled background tasks.

29. Scheduled Tasks — Claude Code workflows can be scheduled to run automatically at defined intervals. Combine this with skills and hooks and you have a self-operating workflow system that runs without manual invocation.

30. Git Version Control — Claude Code integrates with git. Every change it makes can be committed, branched, and rolled back through standard git workflows. This is your undo button. Always have Claude Code working inside a git-tracked project. Before: changes happen and you hope nothing breaks. After: every change is versioned, documented, and reversible.

The One Rule That Matters

Master five concepts before you touch the next five. The shiny object trap — jumping from MCP to subagents to hooks before understanding CLAUDE.md and context windows — is the single biggest waste of time. The gap between people getting real results and people falling behind is not talent. It is reps. Start with file access, prompting, CLAUDE.md, plan mode, and /clear. Everything else builds on those five.

AI Agent Directory - Few Shots LLM Models

2026-04-10T00:00:00-03:00

The AI agent ecosystem is growing fast. Here's a quick directory of notable AI startups and a couple of few-shot LLM models worth knowing about. Two lines each — just enough to know what they do and why they matter.

AI Agent Directory

Can of Soup — An AI-powered app that lets you create fictional photos of you and your friends in imaginary scenarios. Built during Y Combinator, it uses generative AI to place people into any meme, outfit, or movie scene.

Deepgram — A foundational voice AI platform offering speech-to-text, text-to-speech, and voice agent APIs. Their Nova models deliver high accuracy and low latency, supporting 30+ languages for real-time transcription.

Diffuse Bio — Building generative AI for protein design, using diffusion models to engineer new proteins with control and accuracy. Their foundation model DSG-1 can generate 3D protein structures and design binders from user prompts.

Draftaid — An AI-powered CAD tool that converts 3D models into precise 2D manufacturing drawings automatically. It reduces manual drafting time by up to 90%, acting like a copilot for mechanical engineers.

Edgetrace — A YC-backed AI video analytics platform that lets users search camera networks using natural language. Primarily used by law enforcement and transportation for real-time threat detection and suspect identification.

EzDubz — A real-time AI dubbing tool that translates videos, livestreams, and phone calls while preserving the original speaker's voice. Their proprietary models clone voices on the fly and even replicate emotions across 20+ languages.

Exa — An AI-powered search engine and API built for developers and AI agents. Unlike traditional keyword search, Exa uses neural embeddings for semantic understanding, powering tools like Cursor and Lovable.

Guide Labs — Building interpretable AI foundation models that can explain their reasoning and are easy to audit. Their open-source Steerling-8B is an 8-billion-parameter LLM designed for transparency and debuggability.

Infinity AI — Now known as Lemon Slice, they build a video foundation model for human motion and emotion. Their tech generates expressive, talking characters across styles from photorealistic to cartoon.

K-Scale — Building open-source humanoid robots for developers, with models starting at $999. Their integrated software, hardware, and ML stack lets developers focus on building applications for embodied AI.

Sevn — A generative design startup using AI to automate and optimize the creative design process. Users define parameters and constraints, and Sevn generates a range of design options to explore.

Linux Inc — An AI startup focused on bringing intelligent tooling to the Linux ecosystem. They aim to simplify Linux administration and development workflows through AI-powered automation.

Metalware — A copilot for firmware engineers that automates low-level programming for embedded systems. Their binary analysis tool fuzzes ARM-based software to detect defects earlier in the development lifecycle.

Naiver AI — Navier AI provides a web-based platform for running CFD (computational fluid dynamics) simulations at scale. Their AI agents handle geometry cleanup, meshing, solver configuration, and cloud resource management autonomously.

Osium AI — An AI-powered platform that accelerates materials and chemicals R&D for industry leaders. Their software helps engineers design new materials faster, spanning alloys, polymers, textiles, and bio-based materials.

Phind — An AI search engine purpose-built for developers that generates direct, code-inclusive answers to technical questions. It combines real-time web search with specialized models trained on programming languages and frameworks.

Piramidal — Building a foundation model for the brain, trained on a massive corpus of EEG brainwave data. Their AI interprets neural signals for neurological diagnostics, already being deployed in ICU settings.

Playground — A browser-based AI image generation and design platform used by over 9 million users. It combines text-to-image generation with a full graphic design suite for logos, social media posts, and more.

PlayHT — An AI voice generation platform that offered ultra-realistic text-to-speech with 900+ voices in 142 languages. Known for voice cloning and custom voice creation through deep learning algorithms.

Sonauto — An AI music editor that turns prompts, lyrics, or melodies into full songs in any style. It supports thousands of styles with full-length songs up to 4.5 minutes, complete with vocals and instrumentation.

Tavus — An AI video personalization platform that creates hyper-personalized videos at scale from a single recording. It uses deep learning for voice synthesis and face cloning to generate thousands of unique video variations.

YonduAI — Building the robotic workforce of the future, starting with logistics automation in warehouses. They deploy humanoid robots with remote teleoperation that gradually transitions to full AI-driven automation.

Yoneda Labs — Building a foundation model for chemical reactions to help chemists optimize drug discovery. Their AI defines parameters like temperature, concentration, and catalyst to make synthesis faster and cheaper.

SyncLabs — An AI lip-sync video generator that creates perfectly synchronized mouth movements from any audio track. Their zero-shot model handles any face in any video context without prior training on specific individuals.

Few-Shot LLM Models

Llama 3.1 — Meta's open-source large language model available in 8B, 70B, and 405B parameter sizes. It supports 128K context length and multilingual capabilities, making it one of the most versatile open-weight models for fine-tuning and deployment.

Mixtral — Mistral AI's open-source mixture-of-experts (MoE) model that activates only a subset of parameters per token for efficient inference. It delivers performance comparable to much larger dense models while being significantly faster and more cost-effective to run.

My GenAI Blogs

2026-01-10T00:00:00-04:00

Why GenAI?

Generative AI has completely changed how I think about software, creativity, and problem-solving. Over the past year, I've gone deep into the world of large language models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents. The pace of change is incredible, and I wanted a place to document what I'm learning as I go.

This blog is that place. I'll be writing about my hands-on experiences with GenAI, the tools I'm experimenting with, things that worked, things that didn't, and the lessons I've picked up along the way.

What I've Been Exploring

My GenAI journey started with using ChatGPT and Claude for day-to-day coding tasks. That quickly evolved into deeper exploration:

Prompt engineering — learning how to get consistent, high-quality outputs from LLMs by structuring prompts effectively.
RAG (Retrieval-Augmented Generation) — building pipelines that ground LLM responses in real data using vector databases and embeddings.
Fine-tuning — adapting pre-trained models for specific tasks and domains.
AI agents — creating autonomous workflows where LLMs can use tools, reason through multi-step problems, and take actions.
Local models — running open-source models like LLaMA and Mistral locally to understand how they work under the hood.

I'm not just reading about these topics. I'm building with them, breaking things, and learning from the results.

What to Expect

I plan to post at least one article a week covering topics like:

Practical tutorials on building GenAI applications
Comparisons of different models and frameworks
Deep dives into concepts like embeddings, tokenization, and attention mechanisms
Real-world use cases and project walkthroughs
Opinions on where GenAI is heading and what matters for developers

Some posts will be short and focused, others will be longer walkthroughs. The goal is to share useful, honest content from a developer's perspective.

Let's Go

I'm excited to start writing and sharing. GenAI is moving fast, and the best way to keep up is to build, experiment, and document. That's exactly what this blog is for.