Developing an Agentic Solution

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Agentic solutions evolve through four levels of sophistication: prompt engineering, context engineering, harness engineering, and loop engineering. Not every use case requires all four. Start with a clear understanding of the problem and its constraints, choose the simplest approach that can meet the requirements, and establish measurable baselines for quality, cost, latency, and user experience. Add context, tools, orchestration, and feedback loops only when the solution demands them. Plan collaboratively, execute iteratively, and use evidence to guide each step forward. 

Agentic solution development has evolved from prompt engineering to context engineering, harness engineering, and, most recently, loop engineering. Each approach introduces practical considerations that help determine when and how to use it. We have seen many organizations struggle to move beyond prompt engineering, even when experienced practitioners lead the effort. The complexity of more advanced agentic solutions is easy to underestimate, and a poorly managed transition can produce suboptimal results. Faced with these challenges, some teams may even revert to simpler approaches. This outcome underscores the importance of careful planning, training, and support throughout the transition. The four stages of agentic solution development are prompt engineering, context engineering, harness engineering, and loop engineering. However, organizations do not always need to implement all four stages to achieve meaningful improvements. The key is to understand your organization's needs and constraints and then adopt the appropriate level of agentic sophistication.

Early agentic solutions focused primarily on prompt engineering: crafting effective prompts that elicit the desired responses from AI models. System and safety prompts remain essential for ensuring that an AI system behaves as expected and follows organizational guidelines. Teams must therefore refine and test prompts continuously to keep them effective and aligned with organizational standards. I prefer to conduct feasibility tests that evaluate prompts in real-world scenarios and verify that their results match the system's intended outcomes. These tests establish baseline metrics for correctness, token usage and cost, latency, and overall user experience. As the solution evolves, the baselines guide prompt improvements and help teams measure whether changes continue to meet their objectives. Without careful management, a growing solution can reduce the effectiveness of its original prompts while increasing token usage and latency, ultimately diminishing efficiency and user experience.

Context engineering is the next stage. It provides the AI system with the information required to understand user input and respond appropriately. This context can include interaction history, user preferences, and relevant external information. Effective context management improves the accuracy and relevance of AI responses, resulting in a better user experience. Teams must monitor and update context continuously because outdated or incorrect information can degrade system performance. I recommend establishing a regular review process that incorporates user feedback and examines system interactions for gaps or inaccuracies. This proactive approach helps preserve the quality and reliability of responses over time. Without context engineering, even well-crafted prompts may produce poor results because the system lacks the background information needed to respond accurately and relevantly.

When we ask an LLM to perform a specific, self-contained task, we do not need context engineering to the same extent as we do for more complex interactions. In these cases, a well-crafted prompt can often guide the model effectively. For example, a prompt that specifies the desired format and length may be sufficient for summarizing a given text, without the need for extensive context engineering.

Next, harness engineering focuses on integrating various components and tools to enable the AI system to perform more complex tasks. This stage involves connecting the AI model with external data sources, APIs, and other software systems. Effective harness engineering enables the system to use these resources efficiently and produce more accurate, comprehensive responses. Teams must design and maintain integrations carefully to prevent bottlenecks and support reliable operation. Otherwise, the system may struggle to use external resources, limiting its performance and utility. Harness engineering often follows a cycle of planning, execution, evaluation, and correction. This cycle supports continuous improvement of the system's integrations and workflows, helping it remain adaptable, efficient, and capable of handling increasingly complex tasks. However, iterative and adaptive harnesses consume additional tokens and computational resources. Teams must balance the benefits of iterative optimization against these costs to maintain efficient, sustainable operation. As forward-deployed engineers, we have seen firsthand the consequences of choosing a framework before fully understanding the solution. This framework-first approach is more than an anti-pattern: it can be fatal to the initiative. It locks the team into premature assumptions, encourages the problem to be reshaped around the framework, and makes fundamental design errors costly to reverse. Teams produce more effective and efficient solutions when they first understand the problem's requirements and constraints and then select or design an appropriate framework.

Lastly, the loop engineering stage focuses on creating feedback loops that enable an AI system to learn and improve continuously. It involves collecting user feedback, monitoring performance, and analyzing interaction data to identify opportunities for improvement. Effective loop engineering helps the system adapt to changing requirements while preserving its relevance and value. Teams need dependable mechanisms for capturing feedback, evaluating it, and incorporating appropriate changes. Without these mechanisms, system performance and user satisfaction may decline over time. Well-designed feedback loops also promote a broader culture of continuous improvement. They are particularly important for autonomous systems, where ongoing adaptation is essential to performance and safety. These systems operate in dynamic environments, so their behavior and strategies must evolve as conditions change. Loop engineering provides a disciplined way to monitor performance, gather evidence, and make iterative improvements. This ongoing process prevents stagnation, improves user satisfaction, and supports the sustainable development of intelligent systems.

Developing agentic AI systems involves many challenges and tradeoffs, including ensuring data quality, managing computational resources, addressing ethical concerns, and maintaining security. Teams must navigate these factors carefully to build reliable, responsible AI solutions. As forward-deployed engineers, we have learned the following lessons.

Understand the problem

Before diving into solution design, it is crucial to thoroughly understand the problem at hand. This involves gathering requirements, identifying constraints, and clarifying the desired outcomes. A thorough understanding of the problem helps teams choose a framework and implementation that align with actual needs, reducing the risk of costly mistakes and rework.

Understand the constraints

In addition to understanding the problem, it is essential to identify and understand the constraints that may affect the solution. These can include technical limitations, resource availability, regulatory requirements, and deadlines. Recognizing these factors early allows teams to design feasible, realistic solutions, avoid preventable roadblocks, and make implementation smoother.

Make a simplified plan

Once the problem is understood and the constraints are identified, the next step is to create a simple plan. The plan should outline the approach, define milestones, and allocate resources. It gives the development process a clear direction, helps the team remain focused, and supports better risk management. The goal is to establish enough shared understanding for the team to co-design and co-build the solution effectively. Iterative discussions, prototypes, and continuous feedback can refine the approach and keep it aligned with project goals. Pursuing a complex solution without first establishing a clear plan and understanding the constraints is unlikely to succeed. Every team member should be able to contribute to planning and decision-making, creating shared ownership of the project's success.

Execute and iterate

Execution is where the plan is put into action. Teams implement the solution according to the defined milestones and allocated resources. However, it is important to recognize that the initial implementation is rarely perfect. Iterative development allows teams to test, evaluate, and refine the solution continuously. Feedback loops, performance monitoring, and regular reviews expose opportunities for improvement and help the solution evolve with changing requirements and constraints. An iterative approach enables teams to respond to unforeseen challenges, optimize performance, and improve outcomes over time.

Review, reflect and evolve

After executing and iterating on the solution, it is important to take a step back and review both the process and its outcomes. Teams should reflect on what worked, which challenges emerged, and what they learned. A critical evaluation reveals opportunities for improvement and turns project experience into insight for future initiatives. Continuous reflection helps the team strengthen its capabilities, the solution mature, and the organization benefit from accumulated knowledge. The code and its supporting processes must enable ongoing review, reflection, and evolution so that the solution remains adaptable and resilient.

Conclusion

Developing an agentic solution is not a race toward the most sophisticated architecture. It is a disciplined process of matching the solution to the problem. Prompt engineering may be enough for focused tasks, while complex and dynamic scenarios may require richer context, an integrated harness, and continuous feedback loops. Each additional capability brings benefits, but it also introduces cost, complexity, and operational risk. Teams should therefore advance deliberately, validate assumptions with measurable outcomes, and resist adopting a framework before understanding the problem. A framework-first decision can fatally constrain the initiative before the team has defined what success requires. By combining sound engineering judgment with collaborative planning and continuous learning, organizations can build agentic solutions that remain effective, efficient, and adaptable over time.