What Google’s 2026 report means for your business
The profit is in agents that ask for permission, not forgiveness.
Welcome to issue #95 of FutureBrief Insights. Three times a week I share practical insights on AI & automation trends, tools, and tutorials for business leaders. If you need support on your technological journey, join our community and get access to group chat, Q&As, workshops, and templates.
Ninjabot delivers ready-to-deploy sales AI and automation tools that allows business operators to stop doing busywork and start managing leverage.
🔮 Today’s insights
Google’s 2026 Agent Report
Google Cloud just released their 2026 AI Agent Trends Report, identifying semi-autonomous agents as the dominant workplace shift. The key finding isn't about faster processing; it's about structure. Successful implementations now treat agents as interns who propose actions, rather than bots that blindly execute them. This validates the human-in-the-loop model I've pushed for years. Automation requires supervision, not abdication.
IBM moves past single-purpose
IBM's latest forecast declares the end of single-task bots. We are seeing a move toward agents with long-term memory and multi-step reasoning capabilities. Instead of just "summarize this email", these agents can read the email, check the CRM, and draft a reply based on the client's history. You can finally automate complex workflows (like customer onboarding) that previously required human intuition to connect the dots.
Agents as a strategic interface
ServiceNow and Snowflake are repositioning agents from simple tools to the primary interface for operations. The trend is clear: you won't log into five different dashboards anymore. You will instruct one agent to pull data from five sources and present the decision to you. This kills tab fatigue. Your team stops switching contexts and starts making decisions.
💡 AI teammate era: hiring digital staff instead of buying software
The highest ROI automations aren’t the ones that run entirely on autopilot.
They are the ones that do 90% of the heavy lifting and then pause for a human to get approval.
What this looks like in practice:
The old way: You spend 10 minutes writing an email.
The wrong way (full autonomy): AI writes and sends the email automatically. (High risk of hallucination or tone deafness).
The good way (semi autonomy): AI drafts the email based on context in your email, CRM, and website info, then places it in your drafts folder and pings you on Slack. You review, edit (30 seconds), and hit send.
This approach drastically reduces implementation risk. You don’t need perfect prompt engineering because you have a human safety net.
Think of your new workflow like a construction site. You are the foreman. The agents are the skilled tradespeople. You don’t lay every brick yourself, but you check the wall before the cement dries.
Google calls it semi-autonomous agents. I call it the intern model. Here are it’s biggest benefits:
Risk reduction: You eliminate the fear of AI hallucinating a discount or insulting a client because nothing leaves the building without a thumbs-up.
Time savings: Reviewing a drafted email takes 10 seconds. Writing it takes 5 minutes. That is a 30x speed improvement without losing quality.
Lower bar: You don’t need perfect AI. You just need AI that is helpful enough to do the first pass.
If you try to build fully autonomous systems immediately, you will fail. Reliability comes from that final human check.
Remember that the goal isn’t replacing humans. It’s to give them superpowers.
I have a deeper take on why full autonomy is a trap for your business. Comment on it on LinkedIn, I’ll be happy to hear your take.
🏺 Hidden Gems
Zapier Central: The SMB answer to enterprise agents. Best for operators who want to teach an agent how to use their HubSpot or Google Sheets data without writing code.
Gemini Code Assist: Build enterprise-grade agents grounded in your own data. Best for larger SMBs (50+ employees) who need strict data governance and compliance controls.
Google Vertex AI Agent Builder: An AI coding partner that understands your entire codebase. Best for technical founders or small dev teams wanting to ship features 40% faster by automating boilerplate code.
Forward this to a colleague who’s wrestling with manual processes. They’ll thank you.
What’s your take on today’s topics? Did you like it, or is there something I missed?
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“They are the ones that do 90% of the heavy lifting and then pause for a human to get approval.”
this 100%. HITL is still more important than ever for critical systems.
Really resonates how you framed “automation requires supervision” and this shift away from 5 different dashboards. That’s exactly where search is headed too: AI answers first, links second.
As Google and other AI search engines start acting more like “answer engines,” visibility isn’t just about ranking blue links anymore, but about being the source those systems trust to quote, summarize, and surface.
There’s a growing practice around this called Answer Engine Optimization (AEO) – basically shaping content so AI systems can easily understand, cite, and reuse it.
A free tool that helped make this more concrete: https://aeoanalyzer.app — it shows how content might be interpreted by AI answer engines and where it’s unclear or thin. Not perfect, but useful for experimenting with the kind of integrated future you’re talking about.