CallMissed Blog
Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
Pin Your Models: A Survival Guide for Unstable AI Defaults in Production
OpenAI swapped the default ChatGPT model on May 5, 2026 — GPT-5.5 Instant replaced GPT-5.3 Instant. The change happened in under two weeks. Anything you were testing on the consumer surface the day before may have behaved differently the day after. This is not a one-off. It is the new default cadenc…
Interruption Handling in Voice Agents: The Hard Problem
The single most common reason voice agents feel "robotic" is not voice quality, latency, or even reasoning quality. It is interruption handling. A human conversation partner stops talking the moment you start. A bad voice agent talks over you, ignores you, or restarts in confusion. Interruption is t…
VAD and Endpointing: Why Your Voice Agent Feels Slow
If your voice agent feels sluggish, the culprit is almost never the LLM. It is endpointing — the silence-detection logic that decides "the user is done speaking, start processing." Most teams over-engineer their LLM stack and under-engineer their VAD and endpointing, then wonder why their pipeline f…
Building Multilingual Voice Agents in 2026
A multilingual voice agent is not a monolingual agent with extra language packs. It is an architectural choice that affects every layer of the stack. In 2026, the teams shipping multilingual voice agents successfully are the ones who treat language as a first-class routing dimension, not an aftertho…
WebRTC for Voice AI: A Practical Primer
WebRTC is the transport that almost every browser-based voice AI runs on. It is also the layer that most application teams treat as a black box until something breaks at 3am. This primer is the minimum viable understanding of WebRTC you need to ship voice agents in 2026 — enough to design well, debu…
Evaluating Voice Agents: Beyond Word Error Rate
Word Error Rate is the most-quoted metric in voice AI and the least useful for evaluating actual voice agents. WER measures STT accuracy on transcribed audio. It tells you nothing about whether your agent answered the user's question, finished the task, sounded natural, or kept the conversation aliv…
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…
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; …
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…