The Infinite Context Window Is a Myth: Context Engineering for AI Agents

Official Schedule Context

Large context windows have become a popular answer to the growing complexity of AI agents. When

agents lose track of details, forget prior decisions, or degrade in reasoning quality, the instinct

is often to add more tokens. In practice, this rarely fixes the problem and often makes it worse.

Bigger context windows increase cost and latency, introduce noise, and amplify failure modes like

lost-in-the-middle effects, context collapse, and brittle summarization. This talk argues that the

real challenge is not context size, but context engineering. In this session, we will explore

practical context engineering techniques for building AI agents that reason reliably over time

without relying on ever-larger context windows. Starting from a stateless agent, we will walk

through progressively more advanced strategies, including short-term and long-term memory,

conversation curation policies, retrieval-augmented generation, and tool-driven context injection.

We will examine common failure modes such as context pollution from tool outputs, brevity bias

during summarization, and reasoning degradation as conversations grow, and show concrete ways to

mitigate them. The talk is grounded in real agent implementations using the Strands Agents SDK and

Amazon Bedrock AgentCore, but the principles apply broadly to any agent framework. This session is

intended for engineers building AI agents beyond simple chatbots who want practical techniques they

can apply immediately.

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