Intro to Agentic AI Implementation That Actually Works

You're in yet another meeting where someone breathlessly declares that AI agents will "revolutionize everything." In the next instant, Marketing's complaining to product because the company's chatbot still can't manage to cancel a subscription. Sounds like the latest breakthrough from a Bay Area superlab hasn't made life easier in your cubical yet.

It's hard to tell who's right in the great AI debate. Maybe it doesn't matter. Neither doomsday predictions nor utopic visions produce any kind of competitive advantage (unless you count the power of negative thinking).

If you want an emotional hot take, catch me at the next Sketch networking happy hour. For now, here's my best guess about how we can use agentic AI to survive and thrive in our careers for as long as possible.

TABLE OF CONTENTS

What's the Deal With Agentic AI?

AI Agents: Copilots, Virtual Coworkers and Killer Droids

How do Agentic Workflows Operate?

Real-World AI Agent Implementation Examples

Sanity Check for AI Agent Implementation

Agentic AI and the Enterprise

The Case For and Against Using Agentic AI

Getting Started With AI Agents

AI Agent Consulting and Agentic AI Development Services

What Is Agentic AI vs. Other AI Types?

After checking with people outside of the marketing department, I'm 95+% sure this (admittedly broad) definition won't embarrass you at the next SaaS conference you attend:

• Autonomous Operation: The agent holds the concept of your intended outcomes, then makes decisions and executes tasks in the service of that goal without the need for continous prompting. They need feedback and course corrections, but they get good at assessing complex situations to weigh tradeoffs and proceed accordingly. 
• Circumstantial Awareness: Dynamic AI agents adjust their approaches based on context. They learn from the times they do receive additional prompting. They can develop a sense of their own role relative to humans and other AI agents.
• Self-Training Capabilities: The systems can go beyond executing tasks as intstructed, generating and trying ideas you haven't fed them, looking for better ways to achieve the job to be done within their constraints. They'll follow orders (hopefully forever), but they won't just follow orders. They grasp they why behind the what to look for a better how.

At their best, AI agents don't just automate existing processes—they do parts of the job faster or better, but still ask for approval or help with judgment calls when appropriate. If this all sounds like what you'd want from a human team member, too, then managing AI agents becomes more like a problem we already know how to solve. It's about finding the right team members, creating cohesion, and giving them what they need to succeed.

The Agentic AI Ecosystem From Copilots to Virtual Coworkers

The agentic AI ecosystem is sophisticated. Different types of AI agents serve different purposes, like how human employees specialize in certain skills or roles. Understanding these distinctions matters because it's not just about choosing the best AI agent, but choosing the right one for your specific challenge.

AI Copilots

AI agents in this category work as personal assistants with actual intelligence. Imagine ChatGPT if it had the contextual awareness and the memory of that coworker who takes flawless notes in every meeting.

These agents excel at amplifying individual productivity. They draft emails, generate code, or summarize documents without losing the crucial nuances that matter to your specific role. The trick here is learning the agent's capabilities so you don't fire off a weird email or introduce code that breaks everything. Give a brilliant copilot agent to someone who treats it like a magic eight ball, and you'll get magic eight ball results.

Workflow Automation Platforms

This is where AI agentic workflows start to shine. Platforms like Microsoft's Copilot Studio and Salesforce's Agentforce focus on automating complex, multi-step processes that traditionally required multiple people, systems, and a fair amount of institutional knowledge.

Imagine an AI agent that can handle customer service requests by pulling information from your CRM, checking inventory systems, coordinating with shipping, and following up with personalized communications. They can do all of this while maintaining the context and escalating appropriately when things get complicated.

These systems don't just replace individual tasks. They orchestrate entire workflows with the kind of coordination that usually requires a very organized human with excellent institutional memory.

The catch? Implementation requires serious attention to change management. These agents are most effective when applied to existing processes, but they also reveal inefficiencies and gaps that organizations might prefer to ignore.

Domain-Specific Solutions

Agentic AI companies are increasingly building purpose-built solutions for specific business domains instead of general solutions that try to be everything to everyone. These are specialists that understand the unique challenges and opportunities in particular industries or functions.

For example, AI-driven customer service systems weigh context, emotional tone, and business priorities to provide genuinely helpful responses. AI agents for software development platforms can write, test, debug, and deploy code while maintaining coding standards and security protocols.

These solutions reimagine entire domains with AI at the core, rather than simply layering intelligence onto existing processes. The results can be genuinely transformative, but they require organizations to think differently about how work gets done.

AI-Native Enterprises

At the most ambitious end of the spectrum, some organizations are reimagining their entire operating models around agentic AI. That is, they're reevaluating the fundamentals of how they do things. This is not the same as using AI agents to automate existing workflows. The possibilities are different, the trade-offs are different, and the required changes are far more substantial.

For example, Duolingo has gone "AI-first," a move that some hate and others admire. They've said they won't hire more people until they're positive they're maximizing productivity with AI.

Virtual Workers (The Next Episode)

Perhaps the most fascinating category is AI agents that function as team members rather than tools. These virtual workers can take on specific roles, maintain context across projects, and contribute to team dynamics in ways that feel genuinely collaborative.

Before you start updating your org chart, it's worth mentioning that we're still in the early stages of understanding how this works in practice. The most successful implementations treat virtual workers as specialized team members with specific strengths and limitations, not as human replacements with better uptime.

Which Type of AI Agent Is Right for Me?

The beautiful thing about this ecosystem approach is that most organizations don't need to choose just one type. The most effective agentic AI strategies combine multiple approaches, starting with individual augmentation and workflow automation before gradually exploring more ambitious implementations.

How AI Agentic Workflows Actually Function

Understanding how AI agentic workflows actually operate is crucial for anyone considering implementation. The gap between theoretical capability and practical reality can be tough to cross.

Think of dynamic AI agents as conductors leading a complex orchestra where some musicians are human, others are traditional software systems, and a few are other AI agents with their own specialties. The conductor doesn't play every instrument, but coordinates the performance, adapts to changing conditions, and ensures that everyone is working toward the same musical goal.

The process typically unfolds in four interconnected stages, though real-world implementation is rarely as linear as flowcharts suggest.

Stage 1: Task Reception and Interpretation

When a user gives an AI agent system a task, the magic begins when the agent tries to go beyond the literal ask and determine what's actually needed. This is harder than it sounds because humans are notoriously imprecise communicators, especially when dealing with complex business challenges, and software has historically taken things very literally.

A sophisticated agentic AI system considers context, infers underlying objectives, and identifies potential complications or dependencies. This is the difference between addressing a specific technical problem and looking into the underlying causes.

Stage 2: Planning and Orchestration

The system breaks down complex objectives into manageable tasks, identifies which specialized agents or tools are best suited for each component, and creates a coordination strategy that accounts for dependencies and potential failure points.

This is what replaces the need for continuous prompting. The AI uses dynamic planning that can adapt as circumstances change, rather than following a predetermined script. If the initial approach hits a roadblock, dynamic AI agents can reformulate their strategy, reallocate resources, and pursue alternative pathways while keeping the overall objective in focus.

The system acts as both strategist and project manager, understanding that the goal isn't just to complete tasks but to achieve the underlying business objective efficiently and effectively.

Stage 3: Execution and Coordination

As needed, multiple AI agents work together, each contributing their specialized capabilities while maintaining awareness of the broader workflow. A customer service scenario might involve one agent analyzing customer history, another assessing current inventory, and a third crafting personalized communication, all while a coordinator agent ensures that the information flows properly and timing aligns.

These systems can share context, negotiate priorities, and even disagree constructively when different agents have conflicting assessments or recommendations. This is how the system can maintain coherence across multiple parallel processes while adapting to new information or changing circumstances in real time.

Stage 4: Learning and Iteration

Perhaps the most crucial aspect of agentic AI is its capacity for improvement through experience. These systems don't just execute workflows; they analyze outcomes, identify successful patterns, and adjust their approaches based on feedback.

This learning happens at multiple levels:

• Individual agents improve their specialized capabilities.
• Coordination mechanisms become more efficient.
• The overall system develops better judgment about when to escalate issues or try alternative approaches.

The feedback loop includes both automated performance analysis and human input, creating a continuous improvement cycle that makes the system more effective over time.

Reality Check and Expectation Mitigation

Real-world implementation involves challenges that the theoretical frameworks tend to gloss over. Agentic AI systems are remarkably good at handling complexity within defined parameters, but they can struggle with genuine ambiguity, organizational politics, and the kind of creative problem-solving that humans excel at.

AI agents are powerful collaborators, not omniscient solutions. They excel at tasks that require coordination, consistency, and the ability to process large amounts of information quickly. They struggle with situations that require profound contextual understanding, creative leaps, or navigation of complex interpersonal dynamics.

The most effective AI agentic workflows are designed with these limitations in mind, creating systems that amplify human capabilities rather than trying to replace human judgment entirely.

3 Real-World Agentic AI Implementation Stories

The gap between conference stage demos and actual business implementation is where the rubber meets the road. This is what matters—where AI comes out of the ivory tower and into the cubicle. Here are a few concrete examples of practical agentic AI implementations that businesses use to evolve.

Note that these are not necessarily Sketch's experience with AI, but examples found using Claude and Perplexity. But which better sources to ask about this kind of thing? The real case studies are linked, so no worries about hallucinations here.

1 – Lenovo: Engineering Productivity and Customer Service Excellence

Lenovo's implementation of agentic AI offers a particularly instructive case study because it demonstrates both ambitious vision and pragmatic execution. The Chinese technology company deployed AI agents in multiple business areas and has shared empirical results in an agentic AI case study:

• Content Generation – Creating content 8x faster to introduce more personalization to customer interactions
• Customer Service – Improved customer service response times by 50% overall, and reduced handling times by 20% to lighten the load on human employees
• Knowledge Assistants – Boosted legal team productivity by 80% and increased accuracy by 45% by automating knowledge sharing, contract reviews, and other workflows
  

The company has moved beyond thinking of AI agents as virtual assistants toward envisioning them as "deputies" that can complete tasks independently. This evolution in thinking—from tool to collaborator—reflects a maturation in understanding what agentic AI can actually deliver.

2 – City of Las Vegas: AI Security Assessments and Threat Detection

The City of Las Vegas hosts massive concerts, sporting events, and, of course, a ton of gambling venues. This makes it an attractive target for cybercriminals. Michael Sherwood, Chief Innovation and Technology Officer of the City of Las Vegas, needed a way to keep his digital assets secure without paying a massive workforce to keep watch 24/7. That's why he turned to agentic cybersecurity AI from Darktrace.

Perhaps unsurprisingly, Michael talks about the technology like more of a teammate than a tool: "Darktrace ActiveAI Security Platform gives us confidence. We know we have an AI teammate that is continually learning how our entire ecosystem operates, strengthening our defenses and looking out for any abnormal activity. "

Just like any team member, the AI agent gradually got more responsibility as it gained more trust. Now Darktrace's AI is responsible for generating and executing response playbooks in real time, rather than following predetermined playbooks made by humans.

3 – AI Agents in Healthcare

Epic, a leading healthtech company, announced more than 100 new AI features in the back half of 2024. They've already given a demo of an AI health professional they hope to unveil before too long. This agent was presumably trained on a large amount of healthcare data. It can also pull patient files, which means it would have to be subjected to extremely strict compliance regulations. 

At the demo, Seth Hain of Epic used the MyChart app to communicate with an AI agent about an imaginary wrist surgery. The agent asked Hain to use his phone's camera so it could "see" his wrist. After observing Hain's mobility, the AI concluded that the recovery was ahead of schedule, but that Hain should still wait a while before playing pickleball again.

It's worth noting that this feature won't be publicly available for some time, largely due to the serious nature of the product and the rigorous regulation that will be involved.

Coming soon to a hospital near you

The Pattern Beneath These Success Stories

What emerges from these real-world implementations is a consistent pattern: agentic AI delivers the most value when it handles coordination, consistency, and information processing while amplifying rather than replacing human capabilities.

The most successful deployments share several characteristics:

• Clear Scope Definition: Organizations that succeed define specific boundaries for what AI agents should and shouldn't handle, avoiding the temptation to treat them as universal solutions.
• Human-AI Collaboration: The best implementations create workflows where humans and agentic AI systems complement each other's strengths rather than competing for territory.
• Iterative Improvement: Successful organizations treat AI agent deployment as an ongoing learning process rather than a one-time implementation project.
• Cultural Preparation: The most effective implementations invest heavily in change management and training, helping teams understand how to collaborate effectively with dynamic AI agents.

These stories illustrate that agentic AI isn't about dramatic transformation overnight; it's about intelligent augmentation that compounds over time, creating competitive advantages through better coordination, consistency, and responsiveness.

The Implementation Reality Check: What Works, What Doesn't, and Why

AI can coordinate complex workflows with superhuman consistency, but they can also fail spectacularly when they encounter select edge cases that no one considered during design. Their simulated ability to weigh context and navigate ambiguity can only go so far.

The Trust-Building Challenge

The most significant barrier to agentic AI adoption is psychological, not technical. Research from McKinsey reveals that customers across all age groups, including digital natives, still prefer human interaction for complex support issues. This preference isn't just nostalgia; it reflects a legitimate concern about AI agents' ability to handle nuanced, emotionally charged, or genuinely unprecedented situations.

For example, a bank might address this challenge by creating an architecture that automatically checks for potential errors or hallucinations before dynamic AI agents communicate with customers. This approach can reduce incorrect responses and gradually build trust, but it would also require significant investment in verification systems and human oversight.

Trust must be earned through consistent performance and transparent limitations. Organizations that succeed with agentic AI are explicit about what their systems can and cannot do, creating realistic expectations rather than overselling capabilities.

Change Management

Implementing AI agentic workflows isn't just about deploying new technology; it's about fundamentally changing how work gets done. Organizations, especially the big ones with a lot to lose, resist change. Change is especially scary when it threatens established roles and relationships.

The most successful implementations invest heavily in change management that goes far beyond training sessions and FAQ documents. They create new incentive structures that reward collaboration with AI agents, establish clear career development paths that incorporate AI collaboration skills, and address the legitimate concerns employees have about how these systems will affect their roles.

Consider what you're asking: for people to trust agentic AI systems with tasks they previously controlled, to learn new ways of working, and to potentially redefine their professional identity... all while maintaining productivity and dealing with their normal workload. Not the easiest mindset shift.

Jorge Amar from McKinsey notes that companies, "need to rewire how functions work so they can get the full value from gen AI agents.” Beyond software functions, this is about technology integration. Businesses need to create new organizational rhythms, communication patterns, and decision-making processes that account for both human and artificial intelligence.

Data Security

Agentic AI systems are voracious consumers of data, and they often need access to sensitive information to function effectively. This creates tension between the system's need for comprehensive data access and the organization's need to maintain security and privacy.

Unlike traditional software that operates within clearly defined data boundaries, dynamic AI agents often require access to multiple systems, the ability to synthesize information across domains, and permission to take actions based on their analysis. Creating security frameworks that enable this functionality without creating unacceptable risks requires consideration of identity management, access controls, and audit trails.

The challenge is compounded by the fact that AI agents can exhibit unexpected behaviors as they learn and adapt. Security frameworks must account not just for intended functionality, but also for the possibility that agents might discover new capabilities or interaction patterns that weren't anticipated during design.

The Small-Scale Success Trap

Many organizations start with small pilot projects that demonstrate the potential of agentic AI, achieve modest success, and then struggle to scale those successes across the broader organization. This pattern is so common that it has its own nickname: "pilot purgatory."

This happens because the conditions that made the pilot successful often don't exist at enterprise scale. Pilot projects typically benefit from dedicated resources, simplified use cases, and teams that are highly motivated to make the technology work. Scaling requires dealing with legacy systems, complex organizational dynamics, and users who may be skeptical or overwhelmed.

Successful scaling of agentic AI requires systematic thinking about the entire organization, not just the specific functions where the technology will be deployed. It means addressing integration challenges, training needs, and cultural adaptation across multiple departments and stakeholder groups.

The Perfectionism Problem

Perhaps the most insidious challenge in agentic AI implementation is the perfectionism trap. Because these systems are intelligent and adaptive, there's a temptation to expect them to handle every possible scenario perfectly from day one. When they inevitably fail to meet these unrealistic expectations, the entire implementation can be labeled a failure.

The most successful organizations treat dynamic AI agents like new team members who need time to learn, make mistakes, and gradually improve their performance. They build feedback mechanisms, create safe environments for experimentation, and maintain realistic expectations about the learning curve.

This requires a fundamental shift in how organizations think about technology implementation. Instead of the traditional approach of defining requirements, building systems, and deploying solutions, agentic AI requires ongoing collaboration, continuous learning, and iterative improvement.

Succeeding with AI agents requires organizations to embrace this messiness while maintaining focus on practical business outcomes. They understand that the goal isn't to create perfect systems, but to build capabilities that improve over time and create sustainable competitive advantages.

Agentic AI, Architectural Updates, and Enterprise Technology

The proliferation of agentic AI is fundamentally altering how we think about technology architecture. We're witnessing a shift from the familiar application-centric model toward something more fluid, interconnected, and frankly, a bit chaotic.

Think of traditional enterprise architecture as a well-organized library where every book has its designated place, and finding information requires knowing exactly which section to visit. Now imagine transforming that library into a dynamic research environment where knowledgeable assistants can synthesize information from across all collections, anticipate what you need before you ask, and coordinate with other assistants to deliver comprehensive insights.

It's time to rethink some of the most basic assumptions about how enterprise technology operates.

From Applications to Agent Ecosystems

Traditional enterprise architecture revolves around discrete enterprise applications (HR systems, CRM platforms, ERP solutions, etc.) each with defined boundaries and specific functions. Agentic AI introduces a different paradigm where dynamic AI agents operate across these traditional boundaries, synthesizing information and coordinating actions in new ways.

Instead of employees learning dozens of different applications, each with its own interface and logic, they interact with AI agents that can navigate the entire technology ecosystem on their behalf. This doesn't eliminate the underlying applications, but it creates a new interaction layer that makes the complexity invisible to end users.

IT leaders are discovering that this shift requires new thinking about data architecture, security models, and system integration. When agentic AI systems can access and coordinate across multiple platforms, traditional approaches to access control and data governance become both more important and more complex.

Three Architectural Patterns: The Evolution Spectrum

Organizations implementing agentic AI are converging on three primary architectural approaches, each with distinct advantages and challenges:

1 – Super Platforms

These represent the next generation of comprehensive business applications that include built-in AI agents as core functionality. In platforms like Microsoft's expanding Copilot ecosystem or Salesforce's Agentforce, agentic AI capabilities are integrated directly into familiar business applications.

The advantage of super platforms is simplicity. Organizations can leverage AI agentic workflows without extensive custom development or complex integration projects.

The limitation is flexibility. You're constrained by what the platform provider has chosen to build and optimize.

2 – AI Wrappers

This approach involves creating intelligent interfaces that allow agentic AI systems to interact with existing enterprise applications without exposing sensitive data or requiring major system modifications. It's like having universal translators that enable dynamic AI agents to communicate with legacy systems while maintaining security and compliance requirements.

AI wrappers can be particularly appealing for organizations with significant investments in existing systems. They provide a path to agentic AI capabilities without requiring wholesale replacement of functioning infrastructure. However, they also introduce additional complexity and potential points of failure.

Not to be confused with AI rappers. (Hard to believe their hands would have been messed up just a few months ago.)

3 - Custom AI Agents

Organizations with specific requirements or unique competitive advantages are building custom agentic AI systems tailored to their particular needs. This approach offers maximum flexibility and the potential for genuine competitive differentiation, but it also requires significant technical expertise and ongoing investment.

Custom development makes sense for organizations with unique processes, specialized domain knowledge, or specific competitive advantages that off-the-shelf solutions can't address effectively.

Making It All Work Together

The most complex aspect of agentic AI architecture is creating systems that can coordinate effectively across different platforms, data sources, and organizational boundaries.

Traditional integration approaches (APIs, data warehouses, enterprise service buses, etc.) remain important, but they're insufficient for the dynamic, context-aware coordination that agentic AI requires. These systems need to share not just data but context, intent, and decision-making authority in ways that current integration patterns weren't designed to handle.

Successful AI agentic workflows require new approaches:

• Identity and Access Management: When AI agents can act on behalf of users across multiple systems, traditional authentication and authorization models become inadequate. Organizations need frameworks that can delegate appropriate authority while maintaining security and audit trails.
• Data Governance: Agentic AI systems often need access to data from across the organization to function effectively, but they also need to respect privacy, compliance, and security requirements. This requires sophisticated data governance frameworks that can balance accessibility with protection.
• Workflow Orchestration: Coordinating actions across multiple systems and stakeholders requires new approaches to workflow management that can handle dynamic conditions, exceptions, and the kind of adaptive behavior that dynamic AI agents exhibit.

Collaboration and the Human Element

Perhaps the most crucial aspect of agentic AI architecture is designing systems that support effective human-AI collaboration rather than simply automating existing processes.

This means creating interfaces that make AI agent capabilities visible and controllable, providing mechanisms for human oversight and intervention, and ensuring that people can understand and influence how these systems make decisions.

The most successful implementations create architectures that amplify human capabilities rather than replacing human judgment. They provide transparency into AI agent decision-making, create opportunities for human feedback and course correction, and maintain clear boundaries around what agentic AI systems can and cannot do autonomously.

This architectural evolution represents one of the most significant shifts in enterprise technology since the advent of cloud computing. Organizations that understand and prepare for these changes will find themselves with substantial competitive advantages. Those that treat agentic AI as just another application to bolt onto existing infrastructure may discover that they've missed the point entirely.

Why Thoughtful Agentic AI Adoption Matters

There should be a greater strategy beyond the technical details and productivity metrics. AI is likely to be more of a revolution than a fad. We can't bury our heads in the sand and hope it passes us by. Instead of jumping on the bandwagon to be cool, how can we use AI to gain a competitive advantage?

Productivity and Compound Effects

Most discussions of AI agents focus on immediate productivity gains—faster customer service, more efficient coding, streamlined administrative processes. These benefits are real and valuable, but they miss the more profound strategic implications.

Agentic AI systems can make existing processes faster, but that's the tip of the iceberg. They also enable entirely new ways of operating that compound over time. Organizations that implement dynamic AI agents effectively will set themselves apart:

• Respond to Market Changes More Rapidly: When AI agentic workflows can synthesize information from across the organization and coordinate responses, companies adapt to changing conditions with unprecedented speed and precision.
• Scale Expertise Across the Organization: AI agents can capture and distribute institutional knowledge in ways that traditional training and documentation never could, effectively multiplying the impact of your best people.
• Maintain Consistency While Preserving Agility: Traditional approaches to ensuring consistency (like detailed procedures, oversight layers, and approval processes) tend to slow organizations down. Agentic AI can maintain standards and consistency while enabling rapid adaptation and innovation.
• Optimize Across Previously Isolated Functions: Because dynamic AI agents can coordinate across traditional organizational boundaries, they enable optimization at the enterprise level rather than just within individual departments or functions.

The Competitive Landscape Shift

As agentic AI companies and platforms mature, we're beginning to see a shift in competitive dynamics. Organizations that master human-AI collaboration are developing advantages that are difficult for competitors to replicate quickly.

Companies with sophisticated AI agents can provide 24/7 support that feels personalized and intelligent, handle complex multi-step processes seamlessly, and resolve issues that traditionally required multiple handoffs and extensive wait times. Such a customer experience creates loyalty and competitive differentiation.

Talent and Capability Evolution

The strategic implications extend to talent strategy and organizational capability development. Agentic AI is creating new roles while transforming existing ones, requiring organizations to think differently about skills, training, and career development.

The most valuable employees are those who can collaborate effectively with AI agents, understand their capabilities and limitations, and know how to orchestrate human-AI teams for maximum impact. Technical skills don't hurt, but they might become less important as AI agents get better at communicating in human languages. There might be a greater need for continued development of judgment, creativity, and interpersonal abilities that complement artificial intelligence. 

Organizations that invest early in developing these capabilities will have significant advantages in attracting and retaining talent. The best people want to work in environments where they can focus on meaningful, creative, and strategically important work rather than routine coordination and administrative tasks.

The Innovation Acceleration Effect

Perhaps the most significant strategic implication of agentic AI is its potential to accelerate innovation cycles. When dynamic AI agents can handle routine research, coordination, and analysis, human teams can focus more time and energy on creative problem-solving, strategic thinking, and breakthrough innovation.

This acceleration effect compounds over time. Organizations that implement agentic AI effectively find that they can explore more ideas, test more hypotheses, and iterate more rapidly than competitors who are still constrained by traditional approaches to coordination and analysis.

The Risk of Inaction

The flip side of these opportunities is the risk of falling behind. As agentic AI capabilities become more sophisticated and widely available, organizations that haven't developed competencies in human-AI collaboration may find themselves at increasingly severe disadvantages.

This isn't about the dramatic "AI will replace all jobs" scenarios that dominate popular discourse. It's about the more subtle but equally important reality that organizations with superior coordination, consistency, and responsiveness will gradually outcompete those without these capabilities.

The window for thoughtful experimentation and capability development is still open, but it won't remain open indefinitely. Organizations that begin developing agentic AI competencies now will have time to learn, iterate, and build sustainable advantages. Those that wait may find themselves trying to catch up in environments where the competitive bar has already been raised significantly.

Getting Started: A Practical Framework for Agentic AI Implementation

The gap between strategic understanding and practical implementation is where many agentic AI initiatives encounter their first major obstacle. It's one thing to appreciate the potential. It's quite another to figure out where to start, how to sequence investments, and what to expect along the way.

Agentic AI implementation is a technology project, but it's also an organizational capability development project.

The Foundation

Before diving into vendor evaluations or technical architectures, successful agentic AI implementations begin with fundamental clarity about objectives, constraints, and success criteria.

• Define Specific Business Outcomes: The most effective implementations start with clear, measurable goals that go beyond "improve efficiency with AI." Successful organizations identify specific pain points and design AI agent solutions to address those particular problems. Are you struggling with coordination bottlenecks, consistency challenges, responsiveness limitations? How do you measure those in a way that matters?
• Understand Your Current State: Agentic AI works best when it can leverage existing organizational knowledge, processes, and data. Successful organizations spend significant time mapping their current workflows, identifying coordination points, and understanding where dynamic AI agents could add the most value.
• Establish Realistic Timelines: Effectively implementing agentic AI requires time for experimentation, learning, and iteration. Organizations that rush the process or expect immediate transformation often end up with disappointing results and unrealistic expectations.

This is when you get clarity before engaging with the complexity.

Build vs. Buy Agentic AI

One of the first major decisions organizations face is whether to build custom AI agents, implement existing platforms, or partner with specialized providers. Each approach has distinct advantages and requirements:

Platform Implementation

Starting with established platforms like Microsoft Copilot, Salesforce Agentforce, or similar solutions offers the fastest path to basic agentic AI capabilities. These platforms provide proven functionality, ongoing support, and integration with existing business applications.

The platform approach works best for organizations that have standard business processes, limited technical development resources, and a preference for immediate onboarding over perfect customization.

In-House Custom AI Development

Building custom AI agents tailored to specific organizational needs offers maximum flexibility and the potential for genuine competitive differentiation. This approach makes sense for organizations with unique processes, specialized domain expertise, or requirements standard platforms can't address.

Custom development requires significant technical expertise, longer implementation timelines, and ongoing investment in maintenance and improvement. However, it can create AI agentic workflows that perfectly match organizational needs and competitive strategy.

AI Development Partners

Working with specialized agentic AI companies or consultants who understand both the technology and implementation challenges can accelerate success while reducing risk. This approach combines external expertise with internal knowledge to create solutions that work effectively in real organizational contexts.

The partnership approach works particularly well for organizations that want custom capabilities but lack internal AI expertise, or that need to implement agentic AI quickly while building internal capabilities over time.

Progressive Approach to AI Implementation Sequencing

The most successful agentic AI implementations follow a progressive approach that builds capability and confidence over time:

• Phase 1: Individual Augmentation: Start with AI agents that augment individual productivity. For example, these might be intelligent assistants to help with research, writing, analysis, or routine coordination tasks. This phase builds familiarity with agentic AI capabilities while delivering immediate value.
• Phase 2: Workflow Automation: Implement AI agentic workflows for specific, well-defined processes that involve multiple steps or stakeholders. Focus on areas where coordination and consistency challenges create clear opportunities for improvement.
• Phase 3: Cross-Functional Integration: Develop dynamic AI agents that can coordinate across different departments or functions, enabling enterprise-level optimization and more sophisticated collaboration between human teams and AI systems.
• Phase 4: Strategic Transformation: Explore how agentic AI can enable new business models, customer experiences, or competitive advantages that weren't possible with traditional approaches.

Risk Mitigation and Success Factors

Successful agentic AI implementation requires careful attention to potential risks and success factors:

• Start Small, Learn Fast: Begin with low-risk pilot projects that can demonstrate value without creating major disruption if they don't work as expected. Use these pilots to build organizational knowledge and confidence, and beware of pilot purgatory.
• Invest in Change Management: The technical implementation is often easier than the organizational adaptation. Successful projects invest heavily in training, communication, and cultural change to help people understand how to work effectively with AI agents.
• Plan for Integration: Agentic AI systems work best when they can access and coordinate across multiple organizational systems. Account for the integration challenges and data governance requirements from the beginning.
• Maintain Human Oversight: Even sophisticated dynamic AI agents require human oversight, especially during the learning and adaptation phase. Build clear escalation paths and oversight mechanisms into your implementation, then keep a human in the loop.
• Measure and Iterate: Successful agentic AI implementations include robust measurement and feedback mechanisms that enable continuous improvement and adaptation based on real-world performance.

The organizations that succeed with agentic AI treat implementation as an ongoing learning process rather than a one-time project. They remain focused on practical business outcomes while building the organizational capabilities needed to leverage increasingly sophisticated AI technologies over time.

Partnering for Intelligent Implementation

At the risk of beating a dead horse, the difference between successful implementation and expensive disappointment is creating an organization with an innovative culture and a willingness to evolve continuously. The technology itself is important, but with access to AI becoming ubiquitous, the organizations that wield it best will gain competitive advantages. 

No doubt, having a massive budget for the best AI agents helps. It's just that developing a good strategy to turn theoretical possibilities into practical realities might be even more important.

Vendor Relationships

The agentic AI landscape is littered with implementations that looked impressive in demonstrations but failed to deliver meaningful business outcomes.

Agentic AI is a capability you develop as much as a product you buy. Success requires partners who understand the intricate dance of organizational change, strategic implementation, and the messy reality of integrating intelligence into complex business environments.

This is precisely why organizations are increasingly turning to specialized partners that combine deep technical expertise with practical implementation experience and a focus on sustainable business outcomes. It's why Sketch Development, once thought of as an agile transformation partner for enterprises, was selected to host the very first AI Tinkerers event in St. Louis. 

Sketch Development = Artificial Intelligence + Real Intelligence

Sketch Development's approach to agentic AI implementation reflects a fundamental understanding that successful AI deployment requires more than sophisticated algorithms. It requires strategic thinking, organizational expertise, and the kind of nuanced understanding that comes from helping organizations navigate complex technological transformations.

Speaking of nuance, we prefer to help our partners achieve continuous evolution instead of taking on a series of transformations one after another. This is the difference between product thinking and project thinking.

Sketch starts with your business challenges, competitive context, and strategic objectives. The question isn't "How can we implement AI agents?" but rather "How can agentic AI create sustainable competitive advantages that align with your organization's unique strengths and market position?"

Sketch's Eight Outcomes

This approach recognizes that every organization has different constraints, capabilities, and opportunities. A dynamic AI agent implementation that works brilliantly for a technology startup may be completely inappropriate for a regulated financial services company. Success requires understanding these nuances and designing solutions that work within real organizational contexts.

Sketch's Expertise With AI

Beyond leading AI meetups, here are a few things we've tinkered with so far:

• AI agent to automate core HR tasks
• AI agent to repurpose content for different media platforms
• AI agent to help college students and administrators manage loans
• AI agent to come up with an idea, write a novel, illustrate it, and produce the audiobook
• Custom AI chatbot builder that learns your brand by reading your website and uploaded materials

Comprehensive Implementation: From Strategy to Execution

Sketch Development's agentic AI services span the entire implementation lifecycle, recognizing that success requires attention to strategic planning, technical execution, and organizational change management:

• Strategic Assessment and Planning: Before writing a single line of code or configuring any platforms, Sketch Development works with organizations to understand their current state, identify the highest-value opportunities for agentic AI, and develop implementation roadmaps that balance ambition with practical constraints.
• Custom Development and Integration: When off-the-shelf solutions aren't sufficient, Sketch Development builds custom AI agents and AI agentic workflows that address specific organizational needs while integrating seamlessly with existing systems and processes.
• Platform Implementation and Optimization: For organizations that benefit from existing agentic AI platforms, Sketch Development provides implementation expertise that ensures these tools are configured, integrated, and optimized for maximum impact rather than basic functionality.
• Change Management and Training: Perhaps most importantly, Sketch Development understands that successful agentic AI implementation requires organizational transformation. They provide the change management expertise needed to help teams adapt, learn, and thrive in environments enhanced by artificial intelligence.

Partnership and the Compounding Effect of Expertise

The value of working with agentic AI specialists like Sketch compounds over time. Initial implementations provide immediate business value, but they also build organizational capabilities that enable increasingly sophisticated applications as the technology and market continue to evolve.

Organizations that partner with experienced providers get results faster. They also develop the internal knowledge and external relationships needed to stay at the forefront of advancing agent-based AI capabilities.

This ongoing partnership model recognizes that agentic AI adoption isn't a destination or a "happily ever after" state. It's a journey of continuous learning, adaptation, and improvement.

The most successful organizations will combine internal business expertise with external technical and implementation knowledge. You already have the internal business expertise, which is how you got to where you are today. We can help fill in the rest.

Next Steps — From Insight to Action

If you took the time to read all of this, thanks. Hit me up on LinkedIn, and maybe I can host you at Sketch for a drink or a hot dog. More importantly, I hope this spurred some thoughts about the genuine potential and practical realities of agentic AI implementation.

To put a bow on this thing (and cudgel that poor horse one last time):

• Success with agentic AI requires more than enthusiasm and budget. It requires strategic thinking, technical expertise, and organizational evolution.
• The agentic AI revolution is already underway, and there's nothing we can do to stop it. The only question is whether your organization will approach the transition thoughtfully or bury its collective head in the sand while your competitors cruise past you.
• The future of work isn't about humans versus machines. (Or at least I desperately hope not.) It's about humans and machines collaborating, each doing the parts they do best, to achieve more than either could alone.
  
• Your results will not be linear. Technological growth is almost always exponential. It's probably best to start now. "Always be compounding," as an agentic finance agent might say.

Sketch's AI specialists can help you tame the wave of agentic AI adoption. They've been learning, experimenting, and even stopping to explain some of this to those of us without their technical prowess. Along with the technical expertise, you'll get access to the strategic business insights that come from years of serving successful enterprises.

This team has helped a cybersecurity startup achieve critical mass. It's led cultural and technological shifts within multiple Fortune 500 companies. Agentic AI is only the latest challenge, and Sketch is undaunted. If you're ready to get started, let's connect.