A practical guide to implementing AI agents
According to Capgemini, 82% of large enterprises plan to integrate AI agents within the next one to three years.
Agentic AI is the next phase of AI that goes beyond input>LLM>output.
It’s about creating digital workers who make decisions, take actions, interact with customers, manage workflows, and even learn over time. These AI agents don’t just follow rules; they adapt, predict, and optimise their behaviour.
Very soon, we’ll see agentic AI move mainstream, with early-stage agents joining teams and powering startups. These agents will join Slack, attend stand-ups, and research competitors. While they’ll need lots of oversight, they’ll be able to write content, code, and optimise workflows.
The hype around AI agents
Agentic AI has been called the “next big breakthrough” in AI transformation, with claims it will enhance employee productivity, increase the speed of decision-making, find trends in big data, and reduce operational costs.
A lot of this hype is grounded in reality. AI will be able to do all of these things (and do them 24/7). And with startups experimenting with cheap AI models, costs aren’t the sticking point they once were.
No surprise that Salesforce, Google, OpenAI, and IBM/Watson are all betting big on agentic AI, releasing platforms and offering cost-benefit analysis to convince leaders to make the switch.
Challenges with AI agents
This all sounds great for business. However, it comes with many technical challenges.
Agents rely on large language models, making it more difficult to reverse-engineer issues when an agent performs multiple tasks with varying outcomes. This introduces interesting cost and risk challenges that are hard to fully grasp before implementation.
This means–don’t rush to abdicate critical tasks to an agent. Instead, approach it as if you were working to develop and build capability – only this time, it’s not a human.
Practical implementation
Tried and true rules still apply. You don’t need to overhaul your entire organisation overnight. Here’s how to dip your toes into the AI agent waters without drowning in complexity:
Start small – Begin with low-risk, high-value tasks like summarisation, data organisation, or research assistance. This allows you to test AI capabilities without impacting critical workflows.
Use AI as an assistant, not a replacement – Position the AI agent as a co-pilot supporting humans, not an autonomous decision-maker. Always have a human review AI-generated outputs, especially for important or sensitive decisions.
Apply guardrails – Set up human-in-the-loop validation, data privacy controls, and monitoring to prevent AI from making errors that could cause harm. Restrict its access to sensitive information and critical functions.
Measure and iterate – Track key metrics like accuracy, time saved, and error rates to ensure the AI is truly adding value. Regularly update prompts, training methods, and workflows based on real-world performance data.
Scale responsibly – Only expand AI agent responsibilities when accuracy and reliability are proven. Introduce AI to higher-value tasks gradually, ensuring ongoing oversight and accountability.
Educate your team: AI agents won’t work unless your team knows how to work with them. Your team will need to manage them.
Don’t build custom technology: Partner with reputable vendors specialising in AI agents (Salesforce, Google, OpenAI, etc.). Their solutions will be battle-tested.
The agentic future will only accelerate
AI agents will power the future of business and continued AI transformation. Whether you’re ready or not, agents will soon redefine your industry and workforce. It’s not a question of whether you should adopt this technology—it’s a question of how you can adapt while balancing speed and risk.
