Moving an AI agent from prototype to production requires more than optimism. This session tackles the "Day 2" engineering challenges of scaling resilient agentic architectures on AWS. Learn practical patterns for handling traffic spikes, optimizing throughput, and controlling costs using Amazon Bedrock models and AgentCore Runtime. We'll cover tool filtering strategies, when multi-agent architectures make sense, how to apply evaluations effectively, and how to harden your APIs against real-world load. Leave with concrete techniques to transform brittle GenAI prototypes into production-grade systems that survive viral launches and demanding enterprise workloads.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Day-2 ops for agentic systems — traffic spikes, throughput optimisation, resilience under load. Tool filtering, multi-agent architectures, evals, API hardening.
Why it matters
Viral launches kill prototypes. The Hacker News front page has destroyed more agent demos than bugs have.
The hard parts
Tool fan-out (agent calls 20 tools in parallel and overwhelms downstream). Retry storms when tools fail. Multi-agent coordination overhead. Backpressure that doesn't propagate cleanly.
Playbook moves
(1) Tool filtering — don't expose all tools to all queries. Reduces token cost and decision space. (2) Circuit breakers per tool, not just per service. (3) Async fan-in patterns where possible.
The surprise
When to use multi-agent vs. single-agent: only when single-agent context is genuinely too big OR when subdomains have different model needs. Otherwise, multi-agent is just complexity tax dressed up as architecture. The default should be single-agent until you can name the specific reason it's not enough.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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