Hi — Jugal here.
This week I published something I’ve been building toward for years: the Agentic Architecture Framework — a practitioner’s method for architecting AI systems on a real data foundation. If you know TOGAF, you’ll recognize the shape. If you don’t, here’s the one-line version:
It’s a disciplined way to answer “where does AI earn its keep here?” — and keep answering it as the system grows.
This edition isn’t a recap of the article. It’s the part I couldn’t fit in it: the deeper cut, the worked example, and something I’m only sharing with you — a self-assessment you can run on your own organization Monday morning.
The framework in 90 seconds
Six components:
- 12 principles — the rules every AI initiative gets judged against (business value first, data quality as prerequisite, evals before enthusiasm, humans in the loop by design, cost as architecture, and seven more)
- The AI Delivery Cycle — six phases plus a preliminary: Vision & Value → Data Foundation
The deeper cut: what “data readiness” actually looks like
The phase that kills the most bad AI projects is Phase B — Data Foundation. Not because data teams fail, but because nobody asks for a verdict. So here’s what a real Data Readiness Assessment looks like, worked through on a realistic example.
Scenario: you want an AI assistant that answers customer questions from your support knowledge base and ticket history.
The assessment asks five questions per data source:
- Quality — Of 50,000 tickets, how many have a correct resolution recorded? (In most orgs: 60–70%. The rest are “closed” with no usable answer.)
- Completeness — Do you have the product documentation the answers depend on? Is it current?
- Freshness — How stale is the oldest article the assistant might quote? If it’s three years old, the assistant will confidently give wrong answers.
- Lineage — Can you trace an answer back to the ticket or article it came from? If not, you can’t fix bad outputs.
- Access & rights — Does the assistant have legal access to all of it? Support tickets often contain customer PII that changes what you’re allowed to do.
The verdict format is binary per source: GO or NO-GO — with conditions. Example: “Ticket history: CONDITIONAL GO — usable after filtering to tickets with verified resolutions (est. 65% of corpus). Knowledge base: NO-GO — 40% of articles outdated; remediation required before training.”
That verdict, written down and signed off, is worth more than any model evaluation. Because a brilliant model on unready data is just a faster way to be wrong.
Subscriber exclusive: the maturity self-assessment
Run this on your organization. Be honest — no one sees the score but you.
Level 1 — Exploring. AI curiosity, no production systems. You’re here if: the AI conversation is mostly conference talks and vendor demos.
Level 2 — Experimenting. Pilots and demos exist. You’re here if: you can name three pilots, but none has a production date.
Level 3 — Piloting. First
Try this week
Pick one AI initiative — just one — and score it against three principles:
- Can you name its business outcome in one sentence? (Principle 1)
- Has anyone issued a written data-readiness verdict? (Principle 2)
- Does it have an eval harness that could catch a regression tomorrow? (Principle 3)
If the answer to any of these is no, you don’t have an AI project yet. You have homework. That’s fine — now you know what it is.
What’s next
Next month: governance that accelerates — what the NIST AI Risk Management Framework looks like running inside a real AI system, not a compliance deck. If this edition resonated, that one’s for you.
As always, reply to this email — I read every one.
— Jugal
The Agentic Enterprise is a monthly newsletter on production AI, agentic systems, and enterprise data architecture. Browse the archive · aideeva.com
production AI workloads. You’re here if: something is live, and governance/HITL is the bottleneck.
Level 4 — Scaling. Multiple AI systems in production. You’re here if: cost control and reuse are your main problems — congratulations, those are good problems.
Level 5 — Leading. AI is a managed capability with feedback loops, mature evals, governed expansion. You’re here if: you can run the full delivery cycle in your sleep.
The uncomfortable pattern: most enterprises score themselves at 2 and plan like they’re at 4. If that’s you, the fix isn’t more ambition — it’s Phase B and Phase D done properly, before anything else.
→ Intelligence Architecture → Evaluation → Governance & Deployment → Operate & Evolve
- A content framework — what you actually produce in each phase (not shelfware: signed-off deliverables like the Data Readiness Assessment and the Evaluation Plan)
- Three reference architectures — RAG, agent, and eval-harness blueprints you tailor instead of reinventing
- Governance mapped to NIST AI RMF — Govern, Map, Measure, Manage, woven through the cycle instead of bolted on after
- A 5-level maturity model — from Exploring to Leading, so you can locate yourself honestly
Read the full article here: The Agentic Architecture Framework
And if you’re the one who has to explain AI to a board: I also published the executive companion this week — How CIOs and CTOs Should Approach AI.
That verdict, written down and signed off, is worth more than any model evaluation. Because a brilliant model on unready data is just a faster way to be wrong.
Thanks for the comment, will get back to you soon… Jugal Shah