AWS Case Study | Aviraj Dongare

How I'd Architect a Cloud-Native SaaS Platform Using AI Agents on AWS to Drive Autonomous Business Growth

Auth & Request
- Amplify (CloudFront+WAF) hosts UI. User signs in via Cognito; UI gets a JWT with groups/claims.
- UI calls API Gateway (REST, TLS) with request_id (idempotency) + JWT.
Orchestration (Compute Layer)
- Lambda Coordinator validates payload (JSON Schema), enforces rate limits by tenant, writes audit row to DynamoDB.
- Short tasks run in Lambda; long/parallel work is fanned out to ECS Fargate workers via SQS.
- All calls tagged with request_id for tracing.
Agentic Decisioning (Bedrock Agents SDK)
- Coordinator builds agent context (tenant features, user segment, latest metrics) and invokes the right agent:
  - Engagement Agent → SNS/Email/SMS (targeted campaigns).
  - Feature Prioritization Agent → Jira APIs (backlog updates & priority).
  - Revenue/Pricing Agent → Lambda pricing APIs (price tests/rollbacks).
  - FinOps Agent → Cost Explorer APIs (budgeting/reallocation actions).
- Guardrails (PII filters, topic limits) + output validators. Risky actions route to a human-in-the-loop queue.
Real-Time & Historical Data
- Kinesis ingests clickstream/telemetry; events land in S3 (bronze).
- Glue jobs clean/denormalize to S3 (silver); gold features are materialized Parquet tables registered in Glue Catalog.
- Athena serves ad-hoc queries, dashboards, and agent tools.
ML Layer
- SageMaker trains models (feature store optional). Artifacts stored in S3; versions logged.
- Lambda Inference loads the latest approved model or calls SageMaker endpoints for low-latency scoring.
- Inference outputs (e.g., propensity, churn risk) are injected back into the agents as part of context.
Action & Persistence
- Agents emit a decision_outcome JSON. Coordinator performs side-effects (Stripe/Jira updates, SNS notifications), persists artifacts (prompts, model refs, tool calls, costs) to S3 (Parquet) and CloudWatch Logs.
Feedback Loop
- User/ops feedback + downstream metrics (adoption, revenue, cost deltas) stream via Kinesis → S3 and feed the next training run.
- Ops dashboards read via Athena/QuickSight.

Why This Matters?

Despite investing in analytics tools and CRMs, teams still struggle to:

Deliver truly personalized customer engagement at scale
Prioritize features that actually drive revenue
Make decisions based on live signals, not lagging reports
Align workflows across product, engineering, and finance

From Business Goals to System Architecture

Instead of waiting on humans to interpret reports, this platform empowers AI agents to act instantly on live data.

Connects real-time user behavior, business KPIs, and ML-driven insights to enable autonomous decision-making
Eliminates manual handoffs across product, engineering, and finance by embedding intelligence into the workflow
Architected as a cloud-native system on AWS, built for scale, security, and speed-to-action

In a world where traditional SaaS is giving way to agentic systems, businesses need platforms that can sense, decide, and act, without waiting for dashboards, spreadsheets, or manual inputs. Product teams still launch features without knowing which ones actually drive revenue. Customer engagement is often triggered too late - after churn signals appear, not before.
And most decisions still rely on static KPIs, not real-time behavior.

Defining the Problem

Problem Statement

The Impact

Clarifying Questions

1. Goals & Metrics

What are the primary business goals this platform should serve like revenue growth, reduced churn, faster decision loops?
Which metrics matter most: retention rate, feature adoption, time-to-decision, or customer engagement depth?

2. User & Workflow Context

Who are the main users of the insights and actions including product managers, sales teams, or ops?
What current tools or manual workflows are they using today to prioritize features or engage customers?

3. Automation Boundaries

What types of decisions are safe for AI agents to make autonomously?
Where do we need human override or visibility?

4. Integration Constraints

How will this platform fit into existing product analytics, CRM, and backend systems?
Are there latency, compliance, or cost limitations we need to factor into system design?

See an AI Agent built by me

End-to-End Flow

Risks & Mitigations

→ Circuit breakers + cached defaults

→ Guardrails + allow-lists + HITL

Hallucination/unsafe actions

→ Budgets, fail-closed on limit breach

Vendor/API outages

→ Contracts + DLQs + expectations checks in ETL

Data quality regressions

Prompt/model drift

Cost overruns

AI System

Reliability

→ Versioning + automatic rollback gates

Identifying and Prioritizing Persona & Problem

User Journey and Pain Points

Metrics to be Gauged

By architecting this autonomous agent-driven SaaS platform, businesses can transform scattered signals and manual decisions into actionable, real-time outcomes. This enables product, customer success, and revenue teams to proactively seize growth opportunities, boost customer retention, and make faster, smarter decisions based on live data and not intuition. Ultimately, this approach doesn't just improve metrics, it unlocks sustainable business performance, accelerates growth, and drives deeper, lasting engagement with customers.