# Daily Hermes + AI Research Brief — May 18, 2026

## Important updates

- **Hermes Agent v0.14.0 “Foundation Release” landed May 16.** GitHub release snippets report a large jump since v0.13.0: **808 commits, 633 merged PRs, 1,393 files changed**. This is worth a safe update audit for Will’s production Atlas gateway, but not an automatic update because local config/source changes may exist. Source: [GitHub releases](https://github.com/NousResearch/hermes-agent/releases)

- **Hermes docs now emphasize “profile distributions” for sharing whole agents.** This is directly relevant to Will’s specialist roster: Atlas/default can stay stable while reusable researcher/writer/ops/engineer profile bundles are packaged and replicated. Source: [Hermes profile distributions docs](https://hermes-agent.nousresearch.com/docs/user-guide/profile-distributions)

- **Hermes provider-extension docs are now explicit about auth, runtime resolution, CLI flows, adapters, tests, and docs.** Useful if Will wants clean support for custom/local providers like llama.cpp, GLM/Z.AI, Gemini ACP, LiteLLM routes, or CoreWeave-hosted endpoints. Source: [Adding Providers — Hermes Agent](https://hermes-agent.nousresearch.com/docs/developer-guide/adding-providers)

- **Agent observability is becoming a practical infra pattern.** Red Hat’s OpenTelemetry writeup frames agentic systems as composed of routing agents, specialist agents, LLM inference, MCP servers, and external integrations — basically Will’s Atlas architecture. The takeaway: trace tool calls, model routing, retries, and MCP/server hops as first-class spans. Source: [Red Hat: Distributed tracing for agentic workflows](https://developers.redhat.com/articles/2026/04/06/distributed-tracing-agentic-workflows-opentelemetry)

- **Inference-on-Kubernetes momentum is accelerating.** Red Hat is positioning `llm-d`/AI Inference for managed Kubernetes including CoreWeave/Azure, and Microsoft published a fresh AKS-oriented controllable inference platform pattern covering llama.cpp plus GPU vLLM/TensorRT-LLM workloads. This maps well to Will’s CoreWeave/k8s and local swarm interests. Sources: [Red Hat AI Inference / llm-d](https://www.redhat.com/de/blog/red-hat-ai-inference-brings-llm-d-any-managed-kubernetes-starting-coreweave-and-microsoft-azure), [Microsoft AI Runway on Kubernetes](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/building-a-controllable-inference-platform-on-kubernetes-with-ai-runway/4520590)

- **MCP security/governance keeps surfacing as the unsexy but important agent problem.** Recent posts frame direct agent-to-MCP-server connections as a supply-chain/security risk and call out over-permissioned tools. This matters for Atlas because gateway + local services + specialist profiles can easily accumulate too much ambient authority. Sources: [Box on agent/MCP supply-chain risk](https://blog.box.com/ai-agents-are-creating-new-supply-chain-crisis-we-have-narrow-window-get-it-right), [Kong on MCP tool governance](https://konghq.com/blog/engineering/mcp-tool-governance-security-meets-context-efficiency)

## Actionable ideas for us

- **[quick] Run a read-only Hermes update check later today:** compare local `main` vs `origin/main`, inspect dirty files, and only then decide whether to create a gated update board for v0.14.0.

- **[experiment] Add lightweight OpenTelemetry-style tracing around Atlas workflows:** start with cron jobs, model/provider routing, tool calls, n8n hooks, and local swarm service calls; even JSONL spans would help debug latency and failures.

- **[experiment] Prototype a “profile distribution” export for Will’s specialist roster:** default/Atlas stays production; export stopped/manual profiles like researcher, writer, ops, engineer, reviewer, glm-simple.

- **[watch] Track MCP permission boundaries:** define which profiles may call which local services/tools, especially anything touching filesystem, Telegram/Discord delivery, n8n, credentials, or GPU inference endpoints.

## Worth ignoring

- Generic “best AI agents of 2026” listicles unless they include reproducible benchmarks, cost data, or architecture details.
- Hermes star-count hype without concrete release notes or operational changes.
- Consumer AI app announcements that do not expose APIs, local deployment options, MCP/tool interfaces, or infra lessons.
