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2026-03-16

Daily Framework for 2026-03-16

How I read this page: - [REL] Reliability & Evaluation — What fails in prod? How do we test + observe it? - [AGENT] Agents & Orchestration — What runs the loop? What actions can it take? - [DATA] Data, RAG & Knowledge — Where does context come from? How is it retrieved? - [GOV] Security, Privacy & Governance — What needs policy, permissions, and audit? - [COST] Infra, Hardware & Cost — What gets expensive (latency/tokens/GPU/ops)? How do we cap it? - [OPS] Product & Operating Model — Who owns this weekly? How do we roll it out safely?

Quick system map (to place each item): Model → Context (RAG/memory) → Orchestrator → Tools → Evals/Tracing → Governance.

1) Today's Signals

2) GenAI

Model shifts need a tighter release check

Architectural Implication

  • [REL] Reliability & Evaluation — I should treat a model swap like any other regression risk and rerun the eval pack before rollout.
  • [AGENT] Agents & Orchestration — Keep agent behavior pinned behind flags when model behavior starts moving around.
  • [GOV] Security, Privacy & Governance — Prompt and policy changes that affect decisions should go through approval, not quiet edits.

Retrieval and cost rules need to stay visible

Architectural Implication

  • [DATA] Data, RAG & Knowledge — I want freshness checks separated from answer synthesis so stale context is obvious.
  • [COST] Infra, Hardware & Cost — Track token and latency budget per workflow before usage quietly spreads.
  • [OPS] Product & Operating Model — One owner should review failures, drift, and rollout scope every week.

3) Agentic AI

Agent permissions need a smaller box

Architectural Implication

  • [AGENT] Agents & Orchestration — Start with a smaller tool allowlist and force escalation for anything hard to undo.
  • [REL] Reliability & Evaluation — Multi-step failures need replayable traces, otherwise debugging turns into guesswork.
  • [GOV] Security, Privacy & Governance — Log who approved an autonomous action and what context the system had at that point.

State handling is where production pain shows up

Architectural Implication

  • [DATA] Data, RAG & Knowledge — Memory writes should stay scoped, reviewed, and reversible so context does not get polluted.
  • [COST] Infra, Hardware & Cost — Cap retry depth and tool-call fan-out before long-running tasks get expensive and weird.
  • [OPS] Product & Operating Model — Someone needs to own the runbooks for stuck tasks, bad memory, and retry storms.

4) AI Radar

New capability should enter through a small pilot first

Architectural Implication

  • [REL] Reliability & Evaluation — Start with a narrow eval suite tied to one workflow before opening the gate wider.
  • [GOV] Security, Privacy & Governance — Review data exposure paths before enabling anything new in shared environments.
  • [COST] Infra, Hardware & Cost — Put the pilot behind usage caps so excitement does not turn into surprise spend.

5) CTO Brief

  • Do not widen autonomy before the eval gate is boring and reliable.
  • Keep tool permissions and memory scope tighter than the demo wants.
  • Retries, traces, and approval paths are architecture, not cleanup work.

6) Rohit's Notes

  • The model drifted on structure again. Good reminder that content and layout should not depend on the same step.
  • Today it broke on: GenAI validation failed: expected 2 items.
  • The safer pattern is obvious now: let the model find signals, then let code lock the page shape.

7) Design Drill

Scenario: A platform team wants agent-driven approvals in an internal delivery workflow this quarter.

Constraints: - Existing audit controls cannot be weakened - Cost must stay inside the current platform budget - Failures must fall back to a manual path in minutes

Guiding questions: - Which actions stay read-only by default? - Where is human approval still mandatory? - How will retries be capped and observed? - Which memory writes are allowed and reversible? - Which evals decide whether the pilot expands?

Architecture Implications Index (Today)

  • [REL] Reliability & Evaluation — Component: eval gate; Decision: block rollout until regression checks pass after model or tool changes.
  • [AGENT] Agents & Orchestration — Component: tool policy; Decision: keep permissions narrow and force escalation for sensitive actions.
  • [DATA] Data, RAG & Knowledge — Component: memory layer; Decision: scope writes and make them reversible before long-running use.
  • [GOV] Security, Privacy & Governance — Component: approval audit; Decision: capture actor, context, and decision path for autonomous steps.