Question 1

What's the difference between agents and chat?

Accepted Answer

Chat is request-response: user sends prompt, LLM responds. Agents loop: LLM thinks about goals, picks a tool, executes it, reads the output, decides next steps, repeats until done. Agents can write code, query databases, browse the web, call APIs, pure chat cannot. Agents are significantly harder to build and evaluate.

Question 2

Do I need to know prompt engineering first?

Accepted Answer

Yes. Agent prompts are 5-10x more complex than simple chat: they must specify reasoning steps (chain-of-thought), tool schemas (JSON), error recovery, and decision criteria. You'll spend 40% of your time tuning prompts. Master prompt engineering at intermediate level, then add agent-specific patterns (ReAct, function calling, tool use).

Question 3

Which framework should I learn in 2026, LangGraph, CrewAI, or AutoGen?

Accepted Answer

LangGraph (by LangChain) is the most production-ready and has the largest ecosystem. AutoGen is best for multi-agent orchestration. CrewAI is fastest to prototype but less flexible. Start with LangGraph; CrewAI for rapid demos. AutoGen if your problem is multi-agent coordination (team of specialists). Most jobs in 2026 ask for LangGraph.

Question 4

How do I make agents reliable in production?

Accepted Answer

Comprehensive logging, timeouts on every API call, fallback tools, structured output validation (Pydantic), human-in-the-loop for critical actions, and continuous evaluation. Most agent failures come from hallucinated tool parameters or LLM timeouts. Use Weights & Biases or LangSmith to trace every execution. Test with adversarial inputs: misleading docs, missing data, contradictory instructions.

Question 5

What's the salary jump for learning agents?

Accepted Answer

L1 backend dev ($80-120k) → L2 agent engineer ($155-200k) is a +$40-80k jump in one year. Agents are newer than ML, so supply is thin and demand is high. Companies are building agent infrastructure faster than they can hire, creating premium salaries through 2027.

Question 6

How does agent memory work?

Accepted Answer

Short-term: conversation context window (usually 4k-200k tokens). Long-term: embeddings stored in a vector DB (Pinecone, Supabase pgvector) or traditional DB (query, retrieve relevant context, prepend to prompt). Trade-off: more context = higher cost and slower inference, but better decisions. Most production agents use hybrid: summary of old messages + full context of last 10.

Question 7

Can I deploy agents serverless?

Accepted Answer

Only for stateless agents with fast execution. Most agents loop unpredictably (depends on tool outcomes), so they're poorly suited to lambda's 15-min timeout. Deploy on Ray (distributed), modal.com (serverless-but-long-lived), or traditional containerized services (EC2, Kubernetes). Cloud run/Lambda work for agent-backed APIs if you pre-compute agent responses and cache.

지역	주니어	미들	시니어
USA	$95k	$155k	$240k
UK	£55k	£85k	£140k
EU	€60k	€90k	€150k
CANADA	C$100k	C$160k	C$250k

지역	주니어	미들	시니어
USA	$95k	$155k	$240k
UK	£55k	£85k	£140k
EU	€60k	€90k	€150k
CANADA	C$100k	C$160k	C$250k

AI Agent Development