Agentic AI: What is it?

Technical Overview

Agentic AI refers to AI systems that can act as “agents”, not just generating text (like GPT or Gemini), but autonomously planning, making decisions, and executing tasks across tools, APIs, and environments. “Instead of being stateless (prompt → response), they maintain context, reason about goals, and interact with external systems, similar to what LLMs do with “memory” features, but extended to support planning, tool use, and long-horizon workflows.

It’s not quite like IDEs such as Cursor or Windsurf eiether (though those are incorporating agentic features), it’s actually even broader than that.

Key traits:

• Autonomy: They can initiate actions, not just react.
• Planning: Break down a user request into sub-tasks (often with a planner module or an internal reasoning step).
• Tool use: Call APIs, databases, or functions via connectors (think function calling in OpenAI, Agents in LangChain).
• Memory: Store/retrieve knowledge (short-term and long-term memory).
• Multi-step reasoning: Execute workflows, not just one-off answers.

In short: it’s LLM + orchestration + external integration = something that behaves more like a junior engineer or virtual assistant than a chatbot.

Example with Amazon SQS

Imagine an Amazon SQS queue that powers a healthcare claims-intake agent:

Producers (EHR webhooks, intake portals, or API Gateway) push a claim_received message into claims-intake.fifo with attributes like priority=high, source=ehr, and an idempotency key.

Agent workers (AWS Lambda or ECS on Fargate) consume messages, extend the visibility timeout while working, and execute an agentic plan:

• Parse & validate the claim payload, fetching related PDFs from S3.
• Extract data with Textract and normalize it into FHIR resources.
• Verify eligibility via a payer API and cross-check member/provider details in DynamoDB.
• Detect anomalies (business rules + LLM tool) and, if needed, request missing docs via SNS.
• Write the canonical record to DynamoDB, emit events to EventBridge, and post a compact status to claims-status.

Safety & reliability

• Idempotent writes using the message key; dedupe repeated deliveries.
• Dead-letter queues (DLQ) catch poison messages, with CloudWatch Alarms for alerts or PagerDuty alerts.
• KMS encryption, VPC-only endpoints, IAM least-privilege; PHI never leaves AWS.
• If LLMs are used, they’re wrapped with Bedrock Guardrails and PII redaction.

Memory & learning

• Short-term working memory: the SQS message + S3 scratchpad.
• Long-term memory: DynamoDB claim history, so the agent doesn’t re-ask for documents.

Outcomes

• Clean claims → claims-clean.fifo
• Exceptions → claims-review
• Status updates sent via SNS/email, with a full audit trail in CloudTrail + structured logs.

Why SQS is perfect here: it provides back-pressure handling for spiky intake, exactly-once semantics with dedupe keys, robust error recovery with visibility timeouts + DLQs, and decouples producers from intelligent agent workers.

Non-Technical Overview

Imagine instead of asking ChatGPT “What’s the weather in North Bend, WA tomorrow?” and copy-pasting it into your calendar, you could say:

“Plan a picnic this weekend if the weather’s good.”

An agentic AI would:

Check the weather API.

See if it’s sunny on Saturday.

Add “Picnic at Green Lake” to your Google Calendar.

Suggest grocery items to buy.

It doesn’t just answer — it acts. Think of it like a smart assistant that can “do things” for you using tools and data, not just give advice.