Agentic AI isn’t just a better model — it’s model + connectors + orchestration. Red Hat’s MCP demos and Red Hat/OpenShift tooling frame agentic systems as a client/server integration problem: models request JSON tool calls and servers hold the credentials and deterministic code that actually run actions. That means teams must design permissions, catalogs, and auditable runtimes, not just prompts.

Operational advice repeats across security and infra voices: start read‑only, stage in sandboxes, require human approval before writes, and version both models and connector artefacts. Multi‑agent experiments show humans shift from doing execution to defining intent and auditing outputs — ship agent UIs and APIs with explicit provenance and escalation paths.

Salesforce’s Agentforce Grid shows a pragmatic pattern: make agentic automation approachable by leaning on a spreadsheet metaphor—rows of data, columns that run prompts/agents, and invocable actions. That surface turns prototyping into an iterative, inspectable, human‑in‑the‑loop process that business users can run and validate.

From a PM perspective, Grid is a prototype‑to‑production bridge: validate prompts and datasets in a sheet, expose run controls and cost estimates (flex credits), and then promote validated sheets as invocable actions or APIs for scheduled workflows.

Context beats model size for developer and infra agents. The Cisco DevNet demo and Cloud Code examples make the same point: give agents the right files, system messages, and manifests (cloud.md / agents.md) and start in 'plan' or 'ask' modes rather than blind auto‑edit.

That product approach reduces risky auto‑changes, makes behavior reproducible across a team, and lets you offer subscription tiers that opt out of training. Mix architecture literacy into PM checklists so model choices map to latency, control, and safety requirements.

Token and credit economics are a core product risk. SecOps practitioners warn token‑heavy workflows (large context, long chaining, detection‑engineering CI runs) quickly blow up API bills. Salesforce’s Grid uses 'flex credits' and ties cost to per‑run model and token usage — a model that forces PMs to think about per‑action pricing and visibility.

The shared refrain: model choice, run frequency, and choreography (combine vs split prompts) directly affect unit economics. Treat billing controls, telemetry, and pre‑run estimates as product features, not ops afterthoughts.

Genie 3 demos (Project Genie) show a new class of creator experiences: sketch → generate → explore → remix. They unlock rapid prototyping and shareable artifacts, but prototypes show clear limits—latency, short session caps, imperfect avatar control and inconsistent mid‑generation editing.

Domain applications (e.g., Togal AI for construction) expose a different lesson: world models plus perception are powerful only when grounded in domain data and human oversight. For high‑cost domains, combine CV perception, structured data, and conservative agentic outputs with editable UIs and long roadmaps.