{ "@context": "https://schema.org", "@type": "DataFeed", "name": "Agentic Bytes Entry Index", "dateModified": "2026-02-19T07:40:32.063Z", "items": [ { "id": "agentic_dev_001", "title": "The Agent Loop: Observe → Plan → Act → Verify", "summary": "Understand the minimal mental model of an AI agent so you can explain it clearly and design it reliably.", "level": "foundation", "tags": [ "agent-loop", "react", "plan-execute", "verification", "reliability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_001.json", "slug": "the-agent-loop-observe-plan-act-verify" }, { "id": "agentic_dev_002", "title": "Tools vs Chat: When an Agent Must Act, Not Just Talk", "summary": "Learn to clearly distinguish between 'thinking in text' and 'acting on the world', and explain why serious agents must use tools.", "level": "foundation", "tags": [ "tool-calling", "hallucination-prevention", "decision-rule", "agent-design" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_002.json", "slug": "tools-vs-chat-when-an-agent-must-act-not-just-talk" }, { "id": "agentic_dev_003", "title": "Output Contracts: Why Agents Must Speak JSON", "summary": "Understand why strict output formats (JSON schemas) are critical for building agents you can trust, debug, and automate.", "level": "foundation", "tags": [ "json-schema", "output-contracts", "agent-reliability", "automation" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_003.json", "slug": "output-contracts-why-agents-must-speak-json" }, { "id": "agentic_dev_004", "title": "Chunking: How Knowledge Must Be Cut for RAG", "summary": "Learn how to structure knowledge so an agent can reliably retrieve and use it without confusion or hallucination.", "level": "foundation", "tags": [ "rag", "chunking", "knowledge-design", "retrieval-quality" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_004.json", "slug": "chunking-how-knowledge-must-be-cut-for-rag" }, { "id": "agentic_dev_005", "title": "Metadata: Teaching Agents What a Chunk Is About", "summary": "Understand how metadata turns raw text chunks into navigable, filterable, and trustworthy knowledge for agents.", "level": "foundation", "tags": [ "metadata", "rag", "knowledge-governance", "agent-control" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_005.json", "slug": "metadata-teaching-agents-what-a-chunk-is-about" }, { "id": "agentic_dev_006", "title": "Reranking: Choosing the Right Knowledge After Retrieval", "summary": "Understand why initial retrieval is not enough and how reranking helps an agent select the most relevant and safe knowledge.", "level": "foundation", "tags": [ "reranking", "rag", "retrieval", "answer-selection" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_006.json", "slug": "reranking-choosing-the-right-knowledge-after-retrieval" }, { "id": "agentic_dev_007", "title": "Guardrails: What an Agent Is Never Allowed to Do", "summary": "Learn how to define hard boundaries so an agent behaves safely, predictably, and does not overstep its authority.", "level": "foundation", "tags": [ "guardrails", "agent-safety", "control", "production-agents" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_007.json", "slug": "guardrails-what-an-agent-is-never-allowed-to-do" }, { "id": "agentic_dev_008", "title": "Self-Check / Critic: Teaching Agents to Verify Themselves", "summary": "Understand how to add an explicit self-check step so agents catch their own mistakes before users do.", "level": "foundation", "tags": [ "self-check", "critic", "verification", "hallucination-control" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_008.json", "slug": "self-check-critic-teaching-agents-to-verify-themselves" }, { "id": "agentic_dev_009", "title": "Plan → Execute: Separating Thinking from Doing", "summary": "Learn why agents must separate planning from execution to stay controllable, debuggable, and safe.", "level": "foundation", "tags": [ "plan-execute", "agent-control", "workflow", "reliability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_009.json", "slug": "plan-execute-separating-thinking-from-doing" }, { "id": "agentic_dev_010", "title": "Human-in-the-Loop: Where Agents Must Stop and Ask", "summary": "Understand where and why an agent must defer to a human, and how to design clear handoff points.", "level": "foundation", "tags": [ "human-in-the-loop", "governance", "agent-autonomy", "trust" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_010.json", "slug": "human-in-the-loop-where-agents-must-stop-and-ask" }, { "id": "agentic_dev_011", "title": "Golden Set & Evals: How to Know Your Agent Works", "summary": "Learn how to evaluate agents systematically so improvements do not break existing behavior.", "level": "foundation", "tags": [ "evaluation", "golden-set", "regression", "agent-quality" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_011.json", "slug": "golden-set-evals-how-to-know-your-agent-works" }, { "id": "agentic_dev_012", "title": "Tracing & Observability: Making Agent Behavior Explainable", "summary": "Understand how to trace, inspect, and explain what an agent did, step by step, in production.", "level": "foundation", "tags": [ "tracing", "observability", "production-agents", "explainability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_012.json", "slug": "tracing-observability-making-agent-behavior-explainable" }, { "id": "agentic_dev_013", "title": "Memory: What Agents Should Remember (and Forget)", "summary": "Understand different types of agent memory and how to use them without creating confusion, drift, or privacy risks.", "level": "foundation", "tags": [ "agent-memory", "rag", "state-management", "knowledge-hygiene" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_013.json", "slug": "memory-what-agents-should-remember-and-forget" }, { "id": "agentic_dev_014", "title": "Prompt Injection & RAG Defense: How Agents Protect Themselves", "summary": "Learn how to prevent agents from being manipulated by user input or retrieved content, especially in RAG systems.", "level": "foundation", "tags": [ "prompt-injection", "rag-security", "agent-safety", "defense" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_014.json", "slug": "prompt-injection-rag-defense-how-agents-protect-themselves" }, { "id": "agentic_dev_015", "title": "Cost & Latency Budgeting: Designing Agents That Are Economical", "summary": "Understand how to design agents with predictable cost and latency, so they are usable at scale and acceptable for business.", "level": "foundation", "tags": [ "cost-control", "latency", "agent-design", "scalability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_015.json", "slug": "cost-latency-budgeting-designing-agents-that-are-economical" }, { "id": "agentic_dev_016", "title": "Versioning: How Agents and Knowledge Evolve Safely", "summary": "Learn how to change agents, prompts, and knowledge without breaking existing behavior or trust.", "level": "foundation", "tags": [ "versioning", "agent-lifecycle", "knowledge-management", "stability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_016.json", "slug": "versioning-how-agents-and-knowledge-evolve-safely" }, { "id": "agentic_dev_017", "title": "Failure Modes & Fallbacks: What Agents Do When Things Go Wrong", "summary": "Understand the most common ways agents fail in production and how to design explicit fallback strategies instead of silent breakdowns.", "level": "foundation", "tags": [ "failure-modes", "fallbacks", "agent-reliability", "production" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_017.json", "slug": "failure-modes-fallbacks-what-agents-do-when-things-go-wrong" }, { "id": "agentic_dev_018", "title": "Single-Agent vs Multi-Agent: When One Brain Is Enough", "summary": "Understand when a single agent is sufficient and when splitting responsibilities across multiple agents makes systems more reliable and maintainable.", "level": "applied", "tags": [ "single-agent", "multi-agent", "architecture", "coordination" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_018.json", "slug": "single-agent-vs-multi-agent-when-one-brain-is-enough" }, { "id": "agentic_dev_019", "title": "Agent Interfaces & Contracts: How Agents Communicate Safely", "summary": "Understand how agents should communicate with other agents and systems using strict contracts instead of free text.", "level": "applied", "tags": [ "agent-interfaces", "contracts", "multi-agent", "architecture" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_019.json", "slug": "agent-interfaces-contracts-how-agents-communicate-safely" }, { "id": "agentic_dev_020", "title": "Ownership, SLAs & Accountability: Who Is Responsible for the Agent", "summary": "Understand how to assign clear ownership and service expectations so agents can be operated like real systems, not experiments.", "level": "applied", "tags": [ "ownership", "sla", "accountability", "operations" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_020.json", "slug": "ownership-slas-accountability-who-is-responsible-for-the-agent" }, { "id": "agentic_dev_021", "title": "Business Value: Where Agents Create Real Impact (and Where They Don’t)", "summary": "Learn to identify use cases where agents generate measurable business value, and avoid areas where they add complexity without payoff.", "level": "applied", "tags": [ "business-value", "roi", "agent-use-cases", "product-thinking" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_021.json", "slug": "business-value-where-agents-create-real-impact-and-where-they-dont" }, { "id": "agentic_dev_022", "title": "From Bytes to RAG: Assembling an Agent Knowledge Base", "summary": "Learn how to turn individual bytes into a coherent RAG knowledge base that agents can reliably use in production.", "level": "applied", "tags": [ "rag-assembly", "knowledge-base", "agent-design", "scalability" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_022.json", "slug": "from-bytes-to-rag-assembling-an-agent-knowledge-base" }, { "id": "agentic_dev_023", "title": "Owning the Knowledge: Turning Bytes into a Personal Moat", "summary": "Understand how structured agentic bytes become a long-term personal asset you can explain, reuse, sell, and build products on.", "level": "strategic", "tags": [ "knowledge-ownership", "personal-moat", "agentic-strategy", "leverage" ], "type": "agentic_byte", "sourcePath": "transfer_datasets_ams_agentic_2026-02-18/agentic-bytes/agentic_dev_023.json", "slug": "owning-the-knowledge-turning-bytes-into-a-personal-moat" } ] }