AI, decoded

How modern AI actually works, in plain English

Short, answer-first explainers on the ideas behind AI agents — context, memory, evaluation, MCP, and more. Each one is drawn from a conversation on Chain of Thought and links straight back to it.

  1. LangGraph vs. CrewAI vs. AutoGen Which AI agent framework should you use — LangGraph, CrewAI, or AutoGen? They make different bets. LangGraph models an agent as an explicit graph of steps and state, so you trade simplicity for fine control. CrewAI organizes work as a 'crew' of role-playing agents with tasks, which is fast to stand up when the work splits cleanly by role. AutoGen centers on conversations between agents, good for open-ended problem-solving. Pick by how much control versus convention you want — and remember the strongest option is often no framework at all for a simple agent. AI AgentsMulti-Agent Systems
  2. Do You Still Need an AI Agent Framework? Do you still need an AI agent framework? Often no. A framework helps you start — it gives you tool-calling, state, and orchestration out of the box — but as the model providers fold those primitives into their own SDKs, the framework's value shrinks. The durable advantage isn't the framework; it's your context: what data you retrieve, how you manage it, and what the system remembers. Many teams start on a framework and then go framework-light as their needs get specific. AI AgentsContext Management
  3. Agentic RAG vs. Traditional RAG What is agentic RAG, and how is it different from regular RAG? Traditional RAG runs one fixed retrieve-then-generate step: fetch documents that match the query, stuff them in the prompt, answer. Agentic RAG puts an agent in charge of retrieval — it decides whether to search, reformulates the query, pulls from multiple sources, checks whether what it got is good enough, and retrieves again if it isn't. The difference is a static pipeline versus a control loop. RAG & RetrievalAI Agents
  4. Agentic vs. Non-Agentic AI What's the difference between agentic and non-agentic AI? Non-agentic AI runs a fixed path: you give it an input, it returns an output, done — a chatbot answering a question, a model classifying a document. Agentic AI runs a loop: it sets a sub-goal, takes an action, observes the result, and decides what to do next, repeating until the task is done. The line is autonomy over the steps. Non-agentic systems follow a path you defined; agentic systems decide the path themselves, which is more capable and far harder to predict. AI Agents
  5. AI Agent Metrics Beyond Accuracy What should you measure on an AI agent besides accuracy? Accuracy tells you whether the final answer was right, but it hides how the agent got there. The metrics that actually predict reliability watch the process: did it pick the right tool, call it correctly, and recover when something failed; how many steps and how much it cost to finish; whether it stayed on the user's intent across a long conversation; and how often it needed a human to step in. An agent can be accurate in a demo and unreliable in production because none of those were measured. AI Evaluation & ReliabilityAI Observability
  6. Are AI Hallucinations a Data Problem? Are AI hallucinations a data problem or a model problem? Largely a data problem. A language model predicts plausible text; when it lacks the right grounding it fills the gap with something that sounds right, which we call a hallucination. Much of that comes from the data layer — missing context, stale or contradictory sources, poor retrieval, no single source of truth. You can't fully train hallucination out of the model, but you can starve it: ground answers in trusted, current data and the model has less reason to invent. The model generates; the data decides whether it has the truth to generate from. AI Evaluation & ReliabilityRAG & Retrieval
  7. Small Language Models in Production Are small language models better than large ones for production? Often, yes — for a specific, well-defined task. A small model that's been tuned for your job can match a frontier model's quality on that job while costing far less, running faster, and being possible to host yourself. The frontier models earn their keep on broad, open-ended reasoning. The mistake is defaulting to the biggest model for everything; the production-smart move is using the smallest model that still passes your evals for each task. AI EngineeringAI Infrastructure
  8. Using AI to Modernize Legacy Code Can AI modernize legacy code and old applications? It can do a lot of the work, but not unsupervised. AI is good at the slow parts of modernization — reading undocumented code, explaining what a function does, translating between languages, and drafting migrations. Where it fails is the part that matters most: it doesn't know the business logic and edge cases the old system quietly encodes, so it will confidently rewrite something subtly wrong. The pattern that works is AI as an accelerator with engineers verifying, plus tests that prove the new code behaves like the old. AI CodingEnterprise AI
  9. How AI Agents Use Tools How does an AI agent decide which tool to use? The agent is given a set of tools, each with a name and a description of what it does and when to use it. At each step the model reads the task and those descriptions and picks a tool, then generates the arguments to call it — a search query, an API payload, a database lookup. It runs the tool, reads the result, and decides the next move. The quality of that choice rides almost entirely on the tool descriptions: vague descriptions produce wrong tool calls, which is one of the most common ways agents fail. AI AgentsAI Evaluation & Reliability
  10. How to Cut AI Agent Costs How do you cut the cost of running an AI agent? Most agent cost is hidden in the steps you can't see: redundant model calls, an oversized model doing a small job, bloated context sent on every turn, and retries from failures nobody caught. You cut it by first making the costs visible with tracing, then attacking the big drivers — route easy steps to a smaller or cheaper model, trim and cache context, cut needless tool calls and loops, and fix the failure modes that cause expensive retries. You can't optimize what you can't see, so observability comes first. Enterprise AIAI Observability
  11. How to Evaluate a RAG System How do you evaluate a RAG system? Evaluate retrieval and generation separately, because they fail differently. For retrieval, ask whether the right documents came back — measure context relevance and recall. For generation, ask whether the answer is grounded in what was retrieved and actually answers the question — measure faithfulness (no claims beyond the sources) and answer relevance. A RAG system can retrieve perfectly and still hallucinate, or generate beautifully from the wrong documents, so a single end-to-end score hides which half is broken. RAG & RetrievalAI Evaluation & Reliability
  12. How to Govern AI Agents in an Enterprise How do you govern AI agents in an enterprise? You govern agents the way you govern any system that takes consequential action: know what they are, control what they can do, and keep a record of what they did. In practice that means an inventory of every agent in production, scoped permissions and approval gates on high-stakes actions, audit trails of decisions and tool calls, and a named owner accountable for each one. The reason it matters now is trust — most leaders don't trust agent outputs, and governance is how you earn the right to deploy them anyway. Enterprise AIAI Agents
  13. How to Test an AI System How do you test an AI system when the output isn't deterministic? You stop expecting one exact answer and start testing properties. Because the same input can produce different valid outputs, traditional assert-equals tests don't fit. Instead you build a dataset of inputs with known-good characteristics and check each output against them — is it grounded, does it follow the instruction, does it avoid the unsafe thing — usually scored by a rubric or an LLM judge. You run that suite on every change, the way you'd run unit tests, so a regression shows up before users do. AI Evaluation & ReliabilityAI Engineering
  14. Is the AI Agent Bubble Real? Is the AI agent bubble real? There's a real gap between the hype and what ships. Demos of autonomous agents are everywhere; reliable agents running unattended in production are rare, and a large share of agent projects never reach production at all. That doesn't mean agents are fake — it means the market priced in capability that the engineering hasn't caught up to yet. The bubble is in the expectations and the timeline, not in the underlying technology. AI AgentsEnterprise AI
  15. Observability vs. Evaluation vs. Benchmarking What's the difference between AI observability, evaluation, and benchmarking? Benchmarking compares models against a fixed dataset before you pick one — it answers 'which model is better in general.' Evaluation measures whether your system does the right thing on your task and your data — 'is this good enough to ship.' Observability is what you run in production — tracing live behavior to see what actually happened when something broke. They answer different questions at different stages, and teams get into trouble by using one where they need another. AI Evaluation & ReliabilityAI Observability
  16. Single-Agent vs. Multi-Agent Architecture Should you build a single agent or a multi-agent system? Start with a single agent. One agent with a clear set of tools is easier to build, debug, and trust, and it handles most tasks. Reach for multiple agents only when the work splits into distinct specialties that benefit from separate context and instructions — and accept that you're trading raw capability for new failure modes: coordination overhead, agents talking past each other, and harder debugging. Multi-agent is a way to manage complexity, not a free upgrade. Multi-Agent SystemsAI Agents
  17. What Are AI Agent Guardrails What are AI agent guardrails, and how do you set them? Guardrails are the limits that keep an autonomous agent inside safe, intended behavior — checks on what it's allowed to do, what it can access, and what it's about to output. They run at three points: on the input (block malicious or out-of-scope requests), on the actions (require approval for high-stakes tool calls, scope permissions), and on the output (catch unsafe, off-policy, or ungrounded responses before they reach the user). You set them by deciding in advance what the agent must never do, then enforcing those rules in code, not in the prompt alone. AI AgentsMulti-Agent Systems
  18. What Is AI Observability What is AI observability, and why do you need it in production? AI observability is instrumenting an AI system so you can see what it actually did on each request — the retrieved context, the tool calls, the intermediate reasoning, the final output — instead of just whether it succeeded or failed. You need it because AI systems are non-deterministic: the same input can behave differently, failures are silent, and a confident wrong answer looks identical to a right one. Without traces of the real behavior, you can't debug, you can't catch drift, and you can't tell a working system from one that's quietly breaking. AI ObservabilityAI Evaluation & Reliability
  19. What Is LLM-as-a-Judge What is LLM-as-a-judge, and when can you trust it? LLM-as-a-judge uses one language model to score the output of another against a rubric — is this answer relevant, grounded, complete, safe. It scales evaluation past what humans can read by hand. You can trust it when you've calibrated it against human judgments on your own data, given it a concrete rubric, and kept a person in the loop for the high-stakes calls. Used blind, it inherits the same biases as the model doing the grading. AI Evaluation & ReliabilityAI Observability
  20. What Is Multimodal AI What is multimodal AI? Multimodal AI is a model that takes in and reasons across more than one kind of data — text, images, audio, video — in a single system. Instead of a separate model for each, one model can read a chart and answer questions about it, transcribe speech and act on it, or describe a video. The hard part isn't handling each modality; it's alignment — getting the model to connect what it sees, hears, and reads into one coherent understanding. Multimodal AIModel Architecture
  21. What Is RAG (Retrieval-Augmented Generation) What is RAG, and why do AI systems use it? RAG, retrieval-augmented generation, is a pattern where the system fetches relevant documents at query time and hands them to the model along with the question, so the answer is grounded in real sources instead of the model's memory. It exists to fix two problems with a bare language model: it doesn't know your private or current data, and it makes things up when it doesn't know. RAG gives the model the right context to read before it answers. RAG & Retrieval
  22. Why Enterprise AI Projects Fail to Show ROI Why do most enterprise AI projects fail to show ROI? Most stall before they ever reach the scale where returns show up. The pilot demos well, then the project hits the costs nobody budgeted: evaluation, integration with messy real systems, data cleanup, governance sign-off, and the ongoing expense of running and monitoring the thing. Add a vague success metric — 'improve productivity' with no baseline — and you get projects that consume budget without producing a number anyone can point to. The failure is usually operational and organizational, not the model. Enterprise AIAI Evaluation & Reliability
  23. On-Premise AI for Regulated Enterprises Why do some enterprises need to run AI on-premise? Because for regulated industries, the data can't leave the building. Sending prompts and documents to an outside AI provider means your sensitive data — patient records, financial data, regulated IP — crosses a boundary your compliance team can't allow. Running the models and the observability stack on-premise, behind your own firewall, keeps the data, the audit trail, and the control inside your perimeter. It costs more and is harder to operate, which is why it's a requirement for the regulated, not a default for everyone. Enterprise AIAI Observability
  24. Why Multi-Agent Systems Fail Why do multi-agent systems fail, and how do you make them reliable? Multi-agent systems fail in the gaps between agents, not inside any one of them. Small per-agent errors compound: a handoff drops context, one agent's wrong output becomes another's trusted input, and a minor fault cascades into a systemic failure no single agent would have produced alone. You make them reliable by treating the system as the unit — tracing every step, validating what passes between agents, setting guardrails on autonomy, and threat-modeling how faults propagate before they reach production. Multi-Agent SystemsAI Evaluation & ReliabilityAI Security
  25. How Much Autonomy Should an AI Agent Have? How much autonomy should you give an AI agent? As much as the risk of the task allows, and no more. There is no single right answer; you climb the ladder one step at a time and decide at each step whether a human still needs to sign off. AI Agents
  26. When AI Hallucinations Are Good (and When They're Dangerous) Are AI hallucinations always bad? No. A hallucination is the model generating something not grounded in fact, and whether that is bad depends entirely on the use. It is a feature for creative work and dangerous for anything factual, with the worst case being an answer that looks right but is wrong in context. AI Evaluation & ReliabilityRAG & Retrieval
  27. 4 Guardrails for Letting Your Whole Company Use AI How do enterprises let employees use AI agents safely? Four guardrails: an allowed list of approved connectors, identity-based authentication, flags on destructive actions, and a human in the loop for anything risky. That is how Block runs AI agents across 12,000 employees at a company handling Square and Cash App. MCP (Model Context Protocol)Enterprise AIAI Security
  28. The 3 Levels of Evaluating an AI Agent How do you evaluate an AI agent? You check it at three levels: the step (did it pick the right tool), the turn (did it do the steps in the right order), and the session (did the whole thing reach the right result). A single accuracy score hides all three, which is why agents that look fine in a demo fail in production. AI Evaluation & ReliabilityAI Agents
  29. Open vs. Proprietary AI Models: When to Use Which Should you use open source or proprietary LLMs? It depends on the job, and most serious teams use both: open when you need control, customization, privacy, or cost efficiency; proprietary when you need top-end quality on certain tasks or the easiest path to start. No one has won the race, so locking into one provider is the mistake. Open Source AIEnterprise AI
  30. Prompt vs. Context vs. Memory Engineering What is the difference between prompt, context, and memory engineering? They are three different jobs, and they happen in order. Prompt engineering is how you word the request. Context engineering is what you put in front of the model for a single task. Memory engineering is what the system keeps and reuses across tasks. Context ManagementAgent Memory
  31. The 4 Types of AI Memory What are the types of AI agent memory? An AI agent needs four kinds of memory, mapped to how the human brain works: working memory for what it is holding right now, semantic memory for the facts it knows, episodic memory for things that happened, and procedural memory for how to do a task. Most AI today runs on only the first one. Agent MemoryAI Agents
  32. 3 Things MCP Unlocks That a Chatbot Can't What can MCP actually do? MCP lets an AI agent connect to your real tools and chain them together, which a plain chatbot cannot do. The value is not in any single connection but in wiring several systems into one workflow. MCP (Model Context Protocol)AI Agents
  33. The 5 Sources of Context an AI Agent Needs What is context in an AI agent? Context is everything you feed a model so it can actually do a task: documents, live web data, structured records, the tools it can call, and the systems where it stores and retrieves. As the models commoditize, the quality of that context is the part that compounds. Context ManagementRAG & Retrieval
  34. 4 Things That Turn a Model Into an Agent What makes an AI agent different from an LLM? An LLM answers; an agent does. The difference is four things built around the model: multiple models orchestrated together, memory and context, tools it can call, and a layer of checks running the whole time. The model is just one part of the system. AI Agents