CallMissed Blog
Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
Building Your First MCP Server: A Step-by-Step Tutorial
The Model Context Protocol (MCP) has gone from an Anthropic side-project announced in late 2024 to the de-facto plumbing for tool-using agents in eighteen months. OpenAI, Google, and most major IDE vendors now speak it natively, and the official spec moved through several revisions in 2025, with a 2…
LangGraph vs OpenAI Agents SDK: Which to Pick
The agent-framework landscape consolidated faster than most people expected. By mid-2026 two names dominate production stacks: LangGraph 1.x from the LangChain team and the OpenAI Agents SDK, released in March 2025 as a production-grade replacement for the experimental Swarm framework. They solve th…
Agent Memory Architecture: Working, Episodic, Semantic
"Agent memory" is one of the most overloaded terms in the field. People mean radically different things: a chat-history buffer, a vector store of past sessions, a fact graph, or some custom hybrid. This matters because picking the wrong memory shape for the wrong job is the most common reason agents…
Agent Evaluation Frameworks: Braintrust, Inspect, Langfuse, and DIY
The hardest question in agent engineering is not "how do I build it?" — frameworks have solved that. It is "is the new version better than the old one?" Without a credible answer, every prompt change is a vibe-check and every model bump is a coin flip. By 2026 the evaluation tooling has matured enou…
Tool Use Design Patterns for AI Agents
The single biggest determinant of agent quality is not the model — it's the tools. A capable model with badly designed tools wanders, retries, hallucinates parameters, and burns tokens. A weaker model with well-shaped tools often outperforms it. Tool design has accumulated a stable set of patterns; …
Computer Use Agents: How They Work and What's Hard
Anthropic introduced Computer Use in late 2024 as the first production-grade API where an LLM could drive a screen — see pixels, move a mouse, type. Eighteen months in, it's no longer a research demo. Production teams are running it for QA automation, internal tooling, RPA-style workflows, and custo…
Autonomous Coding Agents in 2026: Claude Code, Codex, Vibe
Two years ago "autonomous coding agent" meant Devin's first demo and a wave of skepticism. By April 2026 the field has consolidated to a handful of production-grade options — Claude Code, Cursor, OpenAI Codex, Replit Agent 3, and Devin — each with a distinct opinion about how much autonomy is approp…
Multi-Agent Orchestration: When You Actually Need It
"Multi-agent" is the most over-applied label in the agent stack. Most production systems calling themselves multi-agent are really one capable agent with a handful of tools, dressed up. That's not a bad thing — it's usually the correct architecture. Multi-agent orchestration earns its complexity in …
Agent Observability: Tracing Tool Calls End-to-End
You will not debug an agent from logs. The reasoning chain is too branched, the latency surface too rich, and the failure modes too non-local. What you need is a trace — a tree-structured record of every LLM call, tool invocation, retrieval, and decision boundary, with timing and content attached. T…
Cost Budgeting for AI Agents: Stopping the $100 Loop
The single most expensive line in any agent product is the bill from the day a loop ran free. Not the slow accumulation of normal usage — the one Tuesday when a tool retry got into a state where a single conversation called the model 412 times and burned through what was supposed to be a month of ma…
Structured Output vs Tool Use: Which When
By 2026 the "JSON parsing with regex" era is over. Both major model APIs offer constrained-decoding paths that produce schema-valid output, and tool use is mature enough that one or the other handles 90% of structured generation workloads. The remaining question is which to reach for — and the answe…
Agent Handoff Patterns: Specialization at Scale
A handoff is the cleanest multi-agent primitive in 2026 — one agent transfers control to another, carrying conversation context, and the new agent owns the next response. The pattern shows up across frameworks (it's the core abstraction in the OpenAI Agents SDK, and it's expressible in LangGraph as …
Anthropic's claude-agent-sdk: A Practical Walkthrough
The claude-agent-sdk is Anthropic's productized version of the harness that powers Claude Code. It gives you the same agent loop, tool dispatch, and context-management mechanics, programmable in Python and TypeScript. If you've been wiring up tool-use loops by hand against the Messages API, this is …
Browser Automation with AI: Playwright + LLMs in Production
Browser automation went from "Selenium scripts that break every Tuesday" to "an LLM clicking around" faster than most categories. By April 2026 the field has consolidated to a small set of production-grade stacks — Playwright + LLM, Stagehand, Browser-Use, Anthropic Computer Use, and the OpenAI CUA …
AI in Customer Support: What's Actually Working in 2026
Three years after the first wave of generative AI support pilots, the customer service category looks very different from what vendor decks promised. Some deployments are quietly delivering meaningful deflection. Others have rolled back. The honest 2026 answer is "it depends on the intent shape, the…
AI for Sales Call Analysis: Real Results, Real ROI
Conversation intelligence used to be a "nice to have." In 2026 it is the default — most B2B sales orgs above 20 reps record, transcribe, and analyze every customer call. The category has matured past the demo-day pitch into something with measurable impact on win rate and ramp time. Here is what is …
AI in Healthcare 2026: Use Cases That Made It to Production
Healthcare AI in 2024 was mostly pilots. By 2026, three categories have crossed into production at scale, while several others remain stuck in the "promising but not yet deployable" bucket. Here is the working list, with HIPAA caveats called out where they apply. What made it: ambient clinical docum…
AI in Fintech: Fraud Detection and the Compliance Question
Fraud detection is the highest-volume, highest-stakes AI workload in fintech. Every card swipe, account opening, and ACH transfer in 2026 runs through a model that has milliseconds to decide "approve, decline, or escalate." The technology has matured fast — but so has regulator interest in being abl…
AI in Legal: Contract Analysis at Production Scale
Legal AI used to be the canonical "this is not going to work" category — too high-stakes, too much hallucination risk, too entrenched. Then the foundation-model wave broke through, and by March 2026 a single legal AI vendor (Harvey) was raising at an $11 billion valuation. The category is real. So i…