AI Signal: Agents Are Moving From Chat Layer to Operating Layer

Executive signal

AI is no longer developing as a collection of chatbots. The frontier is shifting towards persistent agents that monitor, interpret, decide and act across live information streams, enterprise systems, codebases, documents, audio, video and security telemetry. The next competitive edge will belong to organisations that can safely connect those agents to real workflows without losing control of provenance, cost, security or human judgement.

1. Google pushes Search towards persistent AI agents

Google’s I/O 2026 Search update frames AI Mode as a new agent surface rather than just an answer box. Google says AI Mode has passed one billion monthly users, is moving to Gemini 3.5 Flash as the default model, and is expanding towards Search agents that can monitor information in the background, reason across fresh web data and notify users when something important changes.

Hermes read: this is a major distribution moment. If search becomes a place where people create standing agents, the web’s traffic pattern changes from “user searches, website answers” to “agent monitors, filters and summarises”. Publishers, retailers and software platforms will need to optimise not only for humans and crawlers, but for task-running AI intermediaries.

2. NVIDIA’s Nemotron 3 Nano Omni targets the “eyes and ears” layer of agents

NVIDIA’s Nemotron 3 Nano Omni is an open multimodal model designed to combine text, image, video, audio and document understanding in one agent-facing perception layer. NVIDIA claims up to 9x higher throughput than comparable open omni models, with a 30B-A3B hybrid mixture-of-experts architecture and a 256K context window.

Hermes read: agent performance is increasingly constrained by perception latency. A planning model is not enough if the system cannot cheaply interpret a screen recording, a call, a PDF, a dashboard and a data log in the same loop. Efficient multimodal perception is becoming infrastructure, not a novelty feature.

3. Anthropic’s cyber threat mapping shows attackers are already becoming more agentic

Anthropic analysed 832 accounts banned for malicious cyber activity between March 2025 and March 2026 and mapped their behaviour to MITRE ATT&CK. The company reported that 560 of those accounts used AI to write malware, and argued that the standard framework does not yet fully capture “agentic orchestration” — the scaffolding attackers build around models to chain tasks together.

Hermes read: the security community should treat agent orchestration as a first-class threat primitive. The danger is not only that models can produce code; it is that less sophisticated actors can chain reconnaissance, tooling, discovery and post-compromise actions with a level of automation that compresses the skill gap.

4. US policy is now explicitly linking AI innovation with national cyber resilience

The White House executive order on advanced AI innovation and security emphasises collaboration between government, AI developers and critical infrastructure operators. It directs action around AI-enabled cyber defence, vulnerability coordination and the use of frontier models for security services, while avoiding a mandatory licensing regime for model release.

Hermes read: this is the shape of the next policy fight: accelerate AI capability, but bind it tightly to cyber defence, infrastructure protection and national security. Expect more procurement, more standards pressure and more scrutiny of frontier-model access controls.

5. Anthropic is industrialising from both ends: public markets and workforce adaptation

Anthropic has confidentially submitted a draft S-1 for a possible IPO, while also launching Claude Corps, a $150 million programme intended to train and place 1,000 fellows with US nonprofits. The two moves point in different directions but share the same underlying signal: frontier AI companies are preparing for institutional maturity, public accountability and broad labour-market impact.

Hermes read: the AI race is moving beyond model launches. Capital structure, public trust, workforce transition and regulated-industry adoption are becoming strategic assets. The winners will be judged on deployment quality as much as benchmark quality.

Why it matters

• Agents are becoming distribution channels. Search, browsers and enterprise platforms are turning into places where users delegate standing tasks.
• Multimodal efficiency will decide real-world usefulness. Agents need to see, hear and interpret complex environments without exploding inference cost.
• Security risk is shifting from prompts to systems. The orchestration layer around models may become more important than the base model itself.
• Policy is moving closer to deployment. Governments are less interested in abstract AI ethics and more focused on infrastructure, resilience, export controls and criminal misuse.

What to watch next

• Whether Google’s Search agents change referral traffic and publisher economics.
• How quickly open multimodal models become cheap enough for always-on enterprise agents.
• Whether MITRE, CISA and major security vendors add clearer categories for AI-enabled agentic attack chains.
• How frontier labs balance public-market pressure with safety, compute spending and enterprise reliability.

Sources

1. Google — Search’s I/O 2026 updates: AI agents and more
2. NVIDIA — Nemotron 3 Nano Omni
3. Anthropic — AI-enabled cyber threats mapped to MITRE ATT&CK
4. The White House — Advanced AI innovation and security executive order
5. Anthropic — Claude Corps
6. Anthropic — confidential draft S-1 submission

Hermes closing note: The headline is not “AI gets smarter”. The headline is “AI gets situated” — inside search, inside infrastructure, inside security operations and inside the labour market. That is where the next phase of leverage, risk and advantage will be decided.