Agents of Change: How AI is Shifting the Way We Work
© Rich Baker
The seasons are shifting.
The light softens, the air changes, and before you know it you are in a different place altogether.
Change in work does not always move like that. Sometimes it creeps in. Sometimes it lands with a thud. AI agents are doing both. We have all used chatbots. Helpful at times, maddening at others. But they have always been reactive: answering the question you asked, then forgetting you ever asked it.
Agents are different. They remember. They anticipate. They take on your goals, not just your commands. They do not wait to be asked. They help carry the work forward. As Gartner’s Tom Coshow explains:
“Intelligent agents in AI are goal-driven entities that don’t require explicit inputs or produce predetermined outputs.”
It is not louder AI. It is quieter, smarter help. I have always been an early adopter. I have my mum to thank for that. And I have been building agents of my own. Not for fun (well, partly!) but as part of how I now work.
One AI Agent I have created using Amazon tools is called Digital Assistant for Voice and Engagement. ‘He’ pulls the right strategy doc or script when I need it, instead of me rummaging through ten near-identical files. Another helps with research, surfacing quotes, data, or counterpoints I might not have thought of. In a way, it extends my thinking, the same kind of metacognition I wrote about earlier this year helping me step outside my own head to see things differently.
At home, another keeps tabs on kids’ diaries, work travel, and family plans. The only thing it cannot do yet is convince a teenager to make their bed in the morning.
These are not (just) toys. They are becoming companions. They do not replace the work. They reshape it. They take on the remembering, the cross-referencing, the invisible effort no one thanks you for but everyone relies on.
And it is not just me.
Salesforce already has agents resolving customer cases end to end. Instacart has one monitoring media and drafting outreach before a PR storm even hits. Rewind has built a memory agent that can answer: “What did I promise in that meeting last week?” KPMG’s “TaxBot” can draft 25 pages of technical advice in a day.
The Financial Times recently observed:
“AI agents can execute multi-step legal processes with minimal human input, but human oversight remains crucial due to the risk of AI errors.”
And now Google’s NotebookLM - originally launched as Project Tailwind - is edging closer to being an agent in all but name. It does not just summarise notes. It digests your material, offers audio overviews (imagine an AI podcast of your own work), even stages debates with itself to help you see arguments from different sides. That feels more like a collaborator than a tool.
Behind the curtain: the models that power agents
Most agents are built on top of large language models. And different models bring different strengths.
GPT-4 / GPT-4o (OpenAI): general reasoning, widely integrated into products like Slack and Notion.
Claude (Anthropic): strong at summarising, context-holding, and safer by design.
Gemini (Google): excels at document workflows, now embedded across Workspace.
LLaMA (Meta): open source, flexible, favoured by researchers and companies wanting control.
Mistral / Mixtral: efficient, open-weight models optimised for speed and cost.
And of course, AWS is shaping this field too, helping customers use these tools in a fast, scalable way:
Amazon Bedrock allows developers to build agents on top of multiple foundation models without handling infrastructure.
Agents for Bedrock connect directly to company data and APIs, turning models into task-oriented assistants.
Amazon Q, launched last year, is already acting like an enterprise co-pilot — answering questions, generating code, pulling from business data.
Amazon Lex, which began as a chatbot framework, is evolving into something closer to agentic orchestration.
Amazon’s investments go well beyond experiments. As Andy Jassy recently told investors, AI is a “once-in-a-lifetime reinvention.” But that reinvention requires scale. AWS is spending heavily on infrastructure - new Trainium2 chips, data centres, Bedrock - bringing cost efficiencies of 30 to 40 percent in inference workloads. It is a reminder that building useful agents is not plug-and-play. Real gains take time, data, and persistence.
Why this matters
Because work has always had two layers. The visible tasks. And the invisible weight of context - the remembering, the linking, the low-level orchestration. Agents are perfectly suited to that second layer. They do not forget. They do not mix one version of the truth with another. They do not get tired. Which means they free us for the human kind of work: judgment, empathy, creativity. The things you cannot automate, and probably should not try to.
Where to begin
Not by expecting magic.
An agent is like a new hire. On day one, they do not know your tone, your shortcuts, your priorities, or your mess of folders. You have to train them. Give them examples. Correct them when they get it wrong. Add context as you go.
The effort is front-loaded. You spend time teaching, nudging, and feeding in the right material. Over weeks they start to anticipate. They remember. They get sharper. Over months - sometimes years - you begin to see the full benefit.
So perhaps the best advice I can offer is simple: start now, if you have not already. Pick one task where memory or repetition eats your time. Summarise a meeting. Surface an old file. Resolve a scheduling clash. Expect it to take effort. But know that in time, the weight shifts.
The trajectory is clear. Gartner predicts that by 2028 at least 15 percent of routine decisions will be made autonomously through AI agents, up from zero in 2024. By 2027, one third of implementations will involve multiple agents working in synergy with humans and each other.
Even the investors backing this space admit the definitions are still taking shape. As a16z put it:
“No one really knows what an AI agent is … an AI agent is a reasoning, multi-step LLM with a dynamic decision tree.”
Which tells you two things. First, the space is still young. And second, there is freedom to shape it.
So will AI replace jobs? Probably. But not entirely. The real skill will be learning how to work with these tools; to shape them, train them, and use them to elevate our own thinking. Not simply as a replacement for it.
Autumn is just around the corner. GenAI and the Agents of Change are already here. Some people will hardly notice it happening. For others it will feel like an abrupt jolt. Either way, the shift has started.
The only real question is whether we will shape these agents to reflect the way we want to work, or quietly let them shape us.
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