In a recent LinkedIn post, Andrew Bolis outlines a 10-step roadmap for building AI agents, from defining a purpose to testing performance. This article reports on Bolis’s post and examines how its sequence frames agent development as a process that begins with clear structure.
Bolis’s central recommendation is to decide what the agent is for before choosing implementation details:
“Building AI agents starts with structure, not code.”
Define the job before selecting tools
According to Bolis, the first step is to specify what an agent should do, whom it supports and what output it should produce. He illustrates that starting point with a content research agent that reviews articles and summarizes key points. The example grounds the broader advice in a defined task rather than an open-ended ambition to “build an agent.”
The next steps in Bolis’s roadmap focus on making that task dependable. He recommends structured inputs and outputs, citing Pydantic AI and JSON Schemas, then shaping behavior with a role-based system prompt. The post also names prompt tuning as one option for maintaining consistent behavior. Together, these stages present interface and instruction design as early decisions in the build process.
Add reasoning and context as needed
Bolis then turns to reasoning and tool access, suggesting approaches such as ReAct alongside capabilities like search, code execution or document lookup. For projects that need more than one role, he describes dividing work among agents such as a planner, researcher and writer, with orchestration frameworks to coordinate them.
That expansion is conditional in his account. Multi-agent roles are introduced “if needed,” while speech, vision, memory and a user interface are presented as optional additions. As Bolis notes:
“Decide if the agent needs context from earlier work.”
His discussion of memory points to summary, conversational or vector-based approaches, including retrieval-augmented generation (RAG). The framing suggests that added capabilities should answer a project need: the post does not present every agent as requiring persistent memory, speech or vision.
Make outputs usable and evaluate performance
The final stages move from the agent’s internal setup to delivery and review. Bolis recommends predictable formats such as Markdown, PDF or JSON, and describes a UI or API layer as a way to make an agent accessible as a product. He closes with testing, logs and benchmarks as ways to assess reliability and guide improvement.
The sequence places evaluation alongside the other design steps, rather than treating a working prototype as the finish line. Bolis sums up the intended benefit this way:
“Get the sequence right to build AI agents quickly.”
In Bolis’s view, the roadmap’s practical value lies in ordering decisions: define the result, structure the exchange, add capabilities where they serve the task, then test and refine. His LinkedIn post presents that sequence as a way for builders to approach agent projects with a clearer plan.
📝 About This Content
This article is based on insights shared by Andrew Bolis on LinkedIn.
📅 Originally posted on November 3, 2026 | View original post on LinkedIn →