Thought Leadership

Building custom AI agents: Your digital employees

An AI agent is a software system capable of independently carrying out tasks, making decisions, and pursuing objectives with little to no human input. Unlike basic chatbots, AI agents can leverage tools, retrieve and analyze data, and execute multi-step plans to handle complex problems.

Fully automated systems require AI agents that function like trained employees rather than generic assistants, and here are six steps to help you set up your first agent.

Step 1: Audit current processes

Map every workflow in granular detail before automating anything. Document each step, identify bottlenecks, and clarify where human judgment is essential versus where pattern recognition suffices. This prevents the common mistake of automating incomplete processes, which creates more problems than solutions.

Step 2: Start simple, then scale

Choose your first automation project based on clear value and manageable complexity. Meeting summaries and action item tracking are good starting points - obvious benefits with limited risk. Build confidence and capability before tackling more complex workflows.

Don't over-engineer. Processes that staff won't adopt lead to abandoned initiatives and wasted investment. Match automation complexity to your organization's readiness.

Step 3: Identify the right tools for the job

There are many off-the-shelf AI platforms but first consider your existing capabilities as well as the desired outcome. What level of technical expertise is required (for example code/no code Agent builders), is there a cost attached, can you buy or do you need to build? Each platform has strengths and weaknesses that need to be assessed before starting your project.

Step 4: Create your knowledge banks

Upload everything you'd use to train a new team member - brand guidelines, client profiles, service specifications, call transcripts, historical content samples, procedural documentation. The AI needs this context to deliver outputs consistent with your firm's standards and expertise.

Step 5: Define behavioral parameters

Define how the AI should interact, what tone it uses, what it can decide independently, and when it must escalate to humans. These guardrails prevent generic outputs and ensure consistency across your practice. It’s also important that firms consider client permissions, confidentiality and have internal processes in place to protect against AI errors.

Step 6: Measure your results

Track efficiency gains (time saved, output increased, costs reduced) alongside relationship depth (client satisfaction, engagement levels, advisory conversations versus compliance-only interactions). Feed this back into the AI to keep improving your outputs.

A practical example

The practical applications are transformative. Consider AI-powered meeting intelligence systems that automatically record meetings, extract key decisions, create project management tasks through voice triggers, and analyze team participation patterns. When someone says "Lauren, update the content strategy" during a call, the AI immediately creates that task in the project management system and assigns it appropriately.

This level of automation eliminates the friction that typically causes important action items to fall through the cracks. The system goes further, integrating with HR platforms to track employee contribution patterns and client sentiment analysis, providing managers with objective data about team performance and client satisfaction without manual tracking.

Sophisticated AI systems can extract strategic value from routine interactions that would otherwise be lost. Call recordings become content assets through automatic analysis for common questions and service opportunities. Client conversations reveal cross-selling possibilities through pattern recognition. Team discussions generate innovation pipelines through systematic idea tracking.

The competitive advantage

The businesses that will dominate their markets aren't necessarily those with the most sophisticated AI tools—they're the ones that most effectively integrate AI into their operational DNA. This means thinking beyond individual productivity gains toward systematic transformation.

As Youell emphasizes, "Think systems, not deliverables." The firms that embrace this philosophy will find themselves operating with unprecedented efficiency while their competitors remain stuck in the content creation paradigm.

The technology exists today. The only question is whether businesses will continue playing it safe with basic chatbots or embrace the systematic transformation that custom AI agents make possible. The early adopters are already pulling ahead—and the gap is widening fast.