Conversational Analytics in 2026: 100% of Conversations, Real-Time Sentiment
Modern conversational analytics tools analyze 100% of customer interactions vs the 1-2% manual QA reviews. Here is what to instrument.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Modern conversational analytics tools analyze 100% of customer interactions vs the 1-2% manual QA reviews. Here is what to instrument.
Bun's WebSocket implementation handles 1.2M concurrent connections vs Node's 680K on identical hardware. Where the gap is real, where it isn't, and the production tradeoffs.
GCP Speech-to-Text Chirp at $0.016 per 15s and Vertex Live multimodal pricing change the math. Where Google Cloud's voice stack beats AWS and OpenAI — and where it does not.
Your traces are a security exposure. PHI, credit cards, passwords end up in spans, prompts, and tool args. Here's a layered redaction pipeline that runs before export.
Letta treats the LLM like an OS that manages its own RAM, recall, and archival memory. Here is when this paradigm beats simple vector stores.
Good agent memory needs to forget. Time-decay weights recent memories higher; Ebbinghaus-style curves auto-evict stale entries; TTL tiers keep allergies forever and small-talk for an hour.
Catch out-of-band changes to AI voice infrastructure with Atlantis-driven Terraform PRs, driftctl coverage scans, and a daily \`terraform plan -detailed-exitcode\` cron.
Pre-trained Speech Commands models, ml5.js wrappers, and TensorFlow.js with the WASM/WebGPU backend let you ship a voice agent with wake-word, intent, and tone detection — all client-side.
Where Claude Managed Agents, sandboxes, and MCP tunnels are heading next — longer-running agents, fleets, governance — and how to prepare your platform now.