{"id":4280569,"date":"2025-02-07T19:00:11","date_gmt":"2025-02-07T17:00:11","guid":{"rendered":"https:\/\/www.deptagency.com\/?post_type=article&p=4280569"},"modified":"2025-05-14T16:54:11","modified_gmt":"2025-05-14T14:54:11","slug":"a-practical-guide-to-implementing-ai-agents-2","status":"publish","type":"article","link":"https:\/\/www.deptagency.com\/en-uki\/insight\/a-practical-guide-to-implementing-ai-agents-2\/","title":{"rendered":"A practical guide to implementing AI agents\u00a0"},"content":{"rendered":"
\n\t\n\t\t\n\t\t\t<\/mask><\/g><\/svg>\t\t\t\n\t\t\t\tInsights\t\t\t<\/span>\n\t\t<\/div>\n\t<\/div>\n\tA practical guide to implementing AI agents\u00a0<\/h1>
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\n\t\tJonathan Whiteside\t\t
\n\t\tGlobal EVP Technology\n\t\t\t<\/div>\n<\/div>\n\t
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\n\t\t\t\t\n\t\t\t\t\tDate\t\t\t\t<\/span>\n\t\t\t\t\n\t\t\t\t\tFebruary 7, 2025\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n<\/div>\n\n\n\tAccording to Capgemini, 82% of large enterprises plan to integrate AI agents within the next one to three years<\/a>.\u00a0<\/h2>\n\tAgentic AI is the next phase of AI that goes beyond input>LLM>output<\/em>.

It\u2019s about creating digital workers who make decisions, take actions, interact with customers, manage workflows, and even learn<\/em> over time. These AI agents don\u2019t just follow rules; they adapt, predict, and optimise their behaviour.\u00a0

Very soon, we\u2019ll see agentic AI move mainstream, with early-stage agents joining teams and powering startups. These agents will join Slack<\/a>, attend stand-ups<\/a>, and research competitors<\/a>. While they\u2019ll need lots of oversight, they\u2019ll be able to write content, code, and optimise workflows.\u00a0<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tThe hype around AI agents\u00a0\u00a0<\/h2>\n\tAgentic AI has been called the \u201cnext big breakthrough<\/a>\u201d 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<\/em> be able to do all of these things (and do them 24\/7). And with startups experimenting<\/a> with cheap AI models, costs aren\u2019t the sticking point they once were.\u00a0

No surprise that Salesforce, Google<\/a>, OpenAI<\/a>, and IBM\/Watson<\/a> are all betting big on agentic AI, releasing platforms and offering cost-benefit analysis to convince leaders to make the switch.\u00a0<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tChallenges with AI agents\u00a0<\/h2>\n\tThis all sounds great for business. However, it comes with many technical challenges.\u00a0

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.\u00a0\u00a0

This means\u2013don\u2019t 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.\u00a0<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tPractical implementation\u00a0<\/h2>\n\tTried and true rules still apply. You don\u2019t need to overhaul your entire organisation overnight. Here\u2019s how to dip your toes into the AI agent waters without drowning in complexity:\u00a0

Start small <\/strong>\u2013 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<\/strong> \u2013 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 <\/strong>\u2013 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 <\/strong>\u2013 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 <\/strong>\u2013 Only expand AI agent responsibilities when accuracy and reliability are proven. Introduce AI to higher-value tasks gradually, ensuring ongoing oversight and accountability.\u00a0

Educate your team<\/strong> \u2013 AI agents won\u2019t work unless your team knows how to work with them. Your team will need to manage them.

Don\u2019t build custom technology<\/strong> \u2013 Partner with reputable vendors specialising in AI agents\u00a0 (Salesforce, Google, OpenAI, etc.). Their solutions will be battle-tested.\u00a0<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tThe agentic future will only accelerate\u00a0<\/h2>\n\tAI agents will power the future of business and continued AI transformation<\/a>. Whether you\u2019re ready or not, agents will soon redefine your industry and workforce. It\u2019s not a question of whether<\/em> you should adopt this technology\u2014it\u2019s a question of how <\/em>you can adapt while balancing speed and risk. \u00a0<\/em><\/p><\/div>\n\t\n\t<\/div>\n\n
\n\tON OUR<\/strong> Mind<\/span><\/span> <\/h2>\n\tVIEW ALL INSIGHTS<\/a>\n\t\n\t\t\n\t\t\t
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