Telnyx vs Slack
Side-by-side trajectory, velocity, and editorial themes.
Telnyx is assembling a multi-vendor AI voice stack on infrastructure it owns.
Telnyx's release notes read like a procurement list for voice AI: in the last month it has added TTS engines (Inworld, Rime Coda), STT engines (Soniox, Deepgram Flux, Speechmatics), and LLMs (Minimax M3, Kimi K2.6, GPT-5.4) to its AI Assistants and Inference products. The common thread is on-network processing — every model runs on Telnyx-owned infrastructure rather than being stitched across vendors.
The model menu is now broad enough that the differentiator has shifted from 'which models' to 'how you orchestrate them.' Conversation Workflows — multi-step assistants with conditional routing and per-step model and voice overrides — signals Telnyx moving up the stack from connectivity provider to agent-building platform. Expect the integration cadence to continue while orchestration features deepen.
The next moves likely extend orchestration: more workflow node types, analytics on assistant performance, or first-party tooling that ties the STT/LLM/TTS chain into a single configurable pipeline.
Slack doubles down on Block Kit data primitives and agent-ready surfaces
Slack's developer platform is converging on two tracks: richer in-message data display through new Block Kit blocks (data tables, data visualization, cards, carousels) and infrastructure for AI agents (CLI agent scaffolding, assistant streaming methods, an expanding MCP server). The 4.x CLI and SDK releases are mostly plumbing for those two arcs.
The direction is Slack-as-a-canvas for structured app output and Slack-as-a-surface that agents can both read from and write into. Block Kit is steadily acquiring the primitives a dashboard or report needs inside a message, while the MCP server work exposes Slack actions to external agents.
Expect more Block Kit data and chart primitives plus continued expansion of the MCP server's tool catalog, with the CLI's agent templates as the on-ramp.
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