The Solutions Architect in the Age of Agentic AI
Advice from a Principal Architect on working smarter in the AI era
Hey, Prasad here 👋 I’m the voice behind the weekly newsletter “Big Tech Careers.”
In this week’s article, I share advice from a Principal Architect on working smarter in the AI era.
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As many of you might already know, we are in week 2 of the Become a Solutions Architect (BeSA) cohort on Agentic AI on AWS. As always, in BeSA we focus on both technical and behavioral skills. In this cohort, the theme of the behavioral skills track is "Develop an SA Mindset in the AI Era."
Glad to see the feedback from participants on the value of the behavioral track.
This week's article is based on a conversation between Raj Menon (roleplaying a Junior Architect) and Jeff Escot (roleplaying a Principal Architect) that took place in in the behavioral track last Saturday (starts at the 62-minute mark). Consider this as an advice from a Principal Architect on how to develop the Solutions Architect mindset in the AI era.
Let’s get started!
Consider how easy these days is creating an architect diagram. You open ChatGPT, type a few sentences, and 90 seconds later there’s a full three-tier cloud architecture on the screen. VPCs, subnets, load balancers, RDS. Clean. Polished. Ready to present.
For a lot of Solutions Architects, the demo like these trigger a quiet panic. If AI can do in 90 seconds what takes us a day and a half, what exactly are we here for?
The Architecture AI Can’t Draw
That 90-second diagram didn’t include the customer’s on-prem Oracle database that their team flatly refuses to migrate. Or the compliance requirement to keep all data in Frankfurt. Or the CTO who got burned by Lambda cold-starts two years ago and has had a thing against serverless ever since.
None of that is in a prompt. None of it ever will be.
Solutions Architects navigate the messy, political, budget-constrained, personality-driven reality of a specific customer at a specific moment. That’s the work AI genuinely cannot do.
Think about what happened when calculators arrived. Mathematicians didn’t disappear. The ones who only did arithmetic had a problem. The ones who understood why you solve certain equations, when to apply which model, and what the results actually mean became more valuable than ever. AI is the calculator. The question is whether you’re the architect who only does arithmetic, or the one who understands the math.
What a Real Engagement Looks Like Now
Take a recent healthcare engagement: a mid-size company building a patient data analytics platform on AWS. A year ago, the process looked like this: day one reading HIPAA documentation, day two comparing Redshift versus Athena versus EMR, day three producing a first-draft architecture. Three full days before anything meaningful hit the page.
Here’s how it went instead. A detailed prompt describing the workload, the data volume, the compliance requirements. Two minutes later: a structured comparison table covering strengths, limitations, cost estimates, and HIPAA considerations for each service option.
The output was about 70% of the way there. A starting point, not a finished product.
The AI recommended Redshift Serverless, which is technically solid advice. What it didn’t know is that this customer’s finance team operates on fixed annual budgets and can’t absorb variable monthly costs. It flagged HIPAA compliance at a surface level but missed that the use case involved sharing data with a research university, which triggers an entirely different conversation about de-identification under HIPAA Safe Harbor. It suggested a standard three-AZ deployment, with no way of knowing that this customer’s board mandated active-active multi-region after a previous outage made national news.
The final architecture came together in four hours instead of three days. Higher quality than a solo effort, because the tedious groundwork was already done and all the energy went into the decisions that actually required thinking.
Two Buckets
If the answer can be looked up, service comparisons, reference architectures, IaC templates, first-draft documentation, AI handles it faster and more thoroughly than any individual can.
What stays firmly in human hands: understanding budget politics, reading a room where a skeptical CTO, a cost-watching CFO, and a paranoid CISO all have different definitions of success. Making trade-off calls under real uncertainty. Knowing whether a team can actually operate what you’re about to design for them. Thinking about where the business needs to be in three to five years.
Judgment under ambiguity is what customers pay premium rates for. AI automated the least valuable, most tedious part of the job.
Three Loops, Not One
The cleanest way to picture the new workflow is three loops, each with a different role for AI.
In the discovery phase, before you ever walk into a customer meeting, AI compresses days of research into hours: industry trends, regulatory context, competitor patterns. After the meeting, it can scan your notes and surface the requirements you forgot to ask about.
In the design phase, AI generates the starting point: reference architectures, service options, infrastructure-as-code. Run everything through three questions before it leaves your hands. Does this match the customer’s actual constraints? Have the key claims been verified against primary documentation? Can you defend every decision in a room of skeptics?
In the delivery phase, AI drafts executive summaries, architecture decision records, and presentation outlines. The communication strategy, who hears what, in what order, framed around whose concerns, is yours to own entirely.
Think of AI tools like a team of capable, fast-moving interns. They take direction well and produce solid work. They need briefing, review, and guidance. They amplify what the lead can get done.
The Identity Shift Nobody Talks About
For years, the value of a senior SA was tied up in knowledge. Certifications. Memorized service comparisons. Being the person who could recite EBS volume types from memory at 6pm on a Friday.
AI has changed that completely.
The real value was always synthesis: taking technical capabilities, business requirements, human constraints, and risk tolerance and turning them into a coherent architecture that works in the real world. That requires experience, empathy, creativity, and judgment.
The architects who thrive will use AI aggressively for knowledge-heavy work and invest the freed-up time into deeper customer relationships and sharper strategic thinking. A surgeon uses an MRI and brings clinical judgment. That’s the model.
Three Things to Do This Week
Brief AI on the customer before every engagement. Industry, tech stack, known constraints, what keeps the CTO up at night. The quality of AI’s output is directly proportional to the quality of your briefing. Treat it like onboarding a new team member.
Build a library of power prompts. Questions like “What are the top five operational risks in the first 90 days?” or “If the budget were cut 40%, what would you cut first?” These prompts become repeatable, high-leverage tools you reach for on every engagement.
Never skip validation. AI is confident even when it’s wrong, especially when it’s wrong. Any claim that feeds into a critical architectural decision needs to be checked against primary documentation.
The Bottom Line
The tedious parts of this job are being automated. The strategic thinking, the creative problem-solving, the human trust built over years of showing up and getting it right, are more valuable than ever.
The calculator arrived. Time to do some real math.
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