Agent Architecture and Protocol Strategy Artifacts
Context and Memory Strategy Evaluator
SIMULATEDVerified Jul 2, 2026Long running agent workflows need a deliberate context strategy. This artifact compares how different memory approaches affect cost, fidelity, and overflow risk as the task grows.
Same instrument · three industries pick a use case to reconfigure the run
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
A naive full history approach may preserve context but can become expensive and fragile. Summarization, compression, and handoff patterns reduce load but introduce fidelity tradeoffs, and the right answer depends on the business task and the risk of losing detail.
Approach
The evaluator compares full context, summarization, compression, and subagent handoff across the same growing task. It shows how token load, overflow risk, and fidelity change as conversations lengthen.
Why this way
This connects context design to unit economics, reliability, accuracy, user experience, and operating scalability.
The metric
Tokens and cost per call versus answer fidelity, by strategy.
The trade-off
Higher fidelity costs tokens; aggressive compression eventually hurts the answer.
Outcome
The context strategy to run per use case, with the point where compression starts to hurt.
Full dump
Send the whole history every call.
$66/1k calls
Summarize
Rolling summary + last two turns.
$36/1k calls
Compress
Semantic compression of history.
$39/1k calls
Subagent handoff
Only the current subtask's brief.
$21/1k calls
If you act on this · the call → expected lift → how you'd measure it
The call
Select context strategy by use case, not by platform default.
Expected lift · illustrative
Reduces runaway token cost and overflow risk while preserving enough fidelity for the task.
How you'd measure it
Token cost per task, overflow events, answer quality, context recall, user rework.
It's a dial, not a default
Steering committee takeaway: Context strategy is a cost and fidelity decision. Set it based on the workflow, not enthusiasm for larger windows.
How this is built
Per strategy, context/call grows differently with turns (full = linear, summarize = ~flat, compress = log, handoff = flat small). Cost ≈ context × input price; fidelity and risk are modeled per strategy; overflow triggers when context exceeds a 24k working window. Memory retention applies each policy's eviction rule to a fixed fact set.
Stack: Next.js (static) + shared design system; deterministic client side.
Limitations: this is a modeled comparison. Production use would require actual conversation traces, task level evaluation, memory policies, privacy controls, and retention rules.