Every few years, tech gets a new obsession. This time, the hype is likely to evolve into a new industry standard. Forget smarter CMSs or speedier backends. The real shift revolves around creating AI-agents. These autonomous teammates know how to take initiative and handle workflows that once needed human oversight. And yes, executives across industries are taking notice.
Building AI agents is the blueprint for how future businesses will run. In this article, we’ll break down what makes AI agents tick, how to design them right, and why the companies mastering this discipline today will define the next decade of innovation.
Key takeaways
- The third wave of AI is here. Agentic systems move from generating content to executing goals through reasoning and autonomous action.
- Focus on workflows. Real impact comes from embedding agents into business workflows.
- Look for proven cross-industry expertise. Lazarev.agency’s agentic designs cut research time, level up creativity, and drive ROI.
Are AI agents the future of work and business management?
In 2024, the global AI agents market was valued at $5.4 billion. By 2030, it’s projected to hit $50.3 billion, growing at a 45.8% CAGR. The surge comes from a perfect storm of breakthroughs in natural language processing (NLP), automation demand, and the need for personalized experiences that traditional software simply can’t match.
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But the most telling signal comes from the executive intent. Companies no longer view AI investments as experimental projects or tech vanity plays. Across industries, leaders now recognize that AI agents define their relevance in the market. Here’s proof:
- 88% of executives plan to increase AI budgets in the coming year, driven largely by agentic AI.
- 79% say AI agents are already in use within their organizations.
- Among these adopters, 66% report tangible productivity gains.
- Organizations using customer service AI agents saw a 14% boost in issue resolution per hour and a 9% reduction in handling time.
Why are we witnessing the rise of agentic AI
AI agents didn’t appear overnight. They’re the result of decades of experimentation and iteration that turned static models into autonomous problem solvers. What we’re witnessing today is the third wave of AI evolution. It’s a logical shift from systems that predict to those that act.
At first, AI was basically a tool for recognizing patterns. It could tell you what might happen next quarter, but it couldn’t take action.
Then generative AI showed up and rewrote the rules of the game. Suddenly, machines weren’t just analyzing data. They started creating new stuff from it. Product descriptions, 3D designs, marketing copy, you name it.
And now, we’ve entered the action era, where AI plans and delivers outcomes almost entirely on its own.
How AI agents work
A software that plans and acts on its own seems like magic. But the mechanics behind agentic AI are surprisingly methodical.
AI agents follow the same self-reinforcing rhythm every autonomous system lives by: observe, plan, act.
But to understand it, you need to look under the hood to see what makes these digital coworkers work.
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1. Observe
Before an AI agent can reason, it must perceive. This phase is about collecting relevant signals from the environment: user interactions, application programming interface (API) responses, data streams, and sensor input.
Where automation stops at preprogrammed triggers, agents continuously learn what matters and what doesn’t.
📋 Real-world example: Amazon Alexa’s ambient learning update introduced contextual recall. Now Alexa notices patterns in user routines like adjusting thermostats or lights. Doing so lets it suggest similar actions next time. That’s smart observation in action.
“Think of strong observation as intelligent contextual awareness. Agents that see well now make better decisions later.”
{{Anna Demianenko}}
2. Plan
Once an agent understands its environment, it shifts into planning mode.
This is where LLMs or small language models (SLMs) act like executive brains. They weigh options and sequence actions toward a goal.
📋 Real-world example: GitHub Copilot doesn’t simply autocomplete code. It interprets developer intent, reviews context from multiple files, and generates a plan. It’s constantly planning and replanning as the developer writes.
3. Act
The action phase is where planning meets production.
The agent takes its internal strategy and interfaces with the real world through APIs, CRMs, databases, or software tools. This is also where adaptability separates smart systems from fragile ones.
📋 Real-world example: AutoGPT showcased how agents could autonomously write, test, and iterate on code. Given a goal (“build a simple website”), AutoGPT planned the process, executed tasks through the web, corrected errors, and completed the job. And a continuous feedback loop is what powered the process.
Anatomy of an AI agent
Every functioning AI agent shares the same structure. Below are five components that mirror human intelligence and power AI agents.
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How to make AI agents work and costly missteps to avoid
Creating AI agents that perform as intended is a matter of precision.
Most failures in agentic AI stem from the system being built around the agent instead of the agentic workflow it needs to support.
Here’s how to make your agents operate as expected and what to watch out for.
1. Design around the workflow
Agents fail when they’re built in isolation. Map your process first. Then, take the time to locate points of friction and design agents to remove them.
❌ Avoid: Treating agents as isolated apps.
✅ Do: Reconstruct the full workflow and place the working agent where it adds leverage.
2. Onboard and assess like an employee
Define the agent’s “job” and KPIs. Run structured tests comparing AI outputs to expert judgment and retrain from real usage data.
❌ Avoid: One-time QA or demo testing.
✅ Do: Continuous performance monitoring tied to business metrics.
3. Build observability
Track every input and decision. Neglecting this step means errors compound without you even noticing. Use dashboards and explainability logs to identify logic failures as soon as they surface.
❌Avoid: Measuring only outcomes.
✅ Do: Monitor reasoning at every step.
4. Use agents where reasoning matters
Agents excel in judgment-based tasks. Think user research, trend forecasting, and performance analysis. For rule-based work, simple automation or ML is usually cheaper and safer.
❌ Avoid: Over-automating predictable tasks.
✅ Do: Apply agents where adaptability adds value.
5. Keep humans in the loop
When switching some processes to agentic AI, humans must stay in the feedback loops to validate and guide agents. To ensure efficiency of the human-agent interaction, design interfaces that make AI reasoning visible.
❌ Avoid: “Full autonomy” mindset.
✅ Do: Build human-agent collaboration with clear oversight points.
How businesses across industries are using AI agents today
AI agents are reshaping workflows across industries where that couldn’t be more different: finance, media, legal, retail, and fashion. What unites them is the same underlying principle — intelligent autonomy supersedes repetitive decision-making.
Below, we break down how agentic AI drives digital transformation using real-life examples from Lazarev.agency’s own portfolio.
1. Finance
The finance industry is built on information asymmetry, meaning whoever processes data faster wins. And that’s exactly what Accern.Rhea set out to fix.
We helped Accern design an agentic research platform that does more than summarize data. In particular, it:
- observes real-time market signals,
- plans research pathways through dynamic filtering,
- acts by generating ready-to-send reports and trend summaries.
With a hybrid GUI/prompt interface, users can interact with the system through chat-like language commands or by using visual controls within the interface. The agent switches between datasets, retrieves filings, and builds institutional-grade reports while learning from user edits.
🏅 The outcome: Analysts cut hours of manual research, VC teams gained faster due diligence cycles, and the product helped push the client from Series B to acquisition, raising over $40 million in the process.
2. Media and news
In a world drowning in opinion, curating truth is the next frontier. For VTNews.ai, we designed an AI newsroom that functions much like a network of collaborating agents.
One agent scans 130k+ sources in real time. Another compares coverage from left, right, and center-leaning outlets. A third predicts prospective trajectories of news narratives. Together, they provide readers with a 360° perspective alongside bias analytics and event timelines.
🏅 The outcome: 90% of users reported escaping their “information bubbles”. Within the first month, the platform attracted 85k new users.
3. Legal
Legal teams face an avalanche of new case law every month. Our redesign of eDiscovery Assistant turned what used to be a static database into an intelligent legal research engine.
It now observes new rulings, categorizes them by relevance and jurisdiction, and acts by surfacing the most pertinent cases within seconds. The interface standardizes complex workflows by pulling up rules, checklists, and related precedents in just three clicks. What it achieves is cutting research time by 75%.
🏅 The outcome: One law firm reported recovering $400K in damages after using the redesigned system to uncover precedents others missed.
4. Fashion and retail
Fashion commerce has long been shackled by high-cost, low-speed content creation. Mannequin literally flips the model.
Our team designed a sales and marketing platform for a fashion-tech pioneer that replaces expensive model photoshoots with AI-generated humans wearing real garments.
🏅 The outcome: The platform plans and generates localized assets by adapting poses, backgrounds, and ethnic appearances to match regional markets. For global retailers, that means instant production of campaign visuals fine-tuned for Tokyo or Paris.
It’s your time to build your own AI agent
AI agents run your business while you focus on what’s next. They observe and act with intent. They free teams from repetitive work and multiply expertise across departments.
At Lazarev.agency, an AI product design agency, we don’t theorize about the future of AI. We design it.
With agentic research systems that close $40M funding rounds and legal AI that reduces workflow time by 75%, our work testifies to the real value of well-designed autonomous agents.
If your company is ready to move beyond automation and into autonomy, contact the best San Francisco design agency and let’s build your first AI agent that will get you there.
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