Agent Autonomy & Overhead Simulator

Scenario & Workload

Representative Workload

Workload Steps 6 Steps

Manual Memory Records 24 items

Dynamic Tool Synthesis

Sub-agent Hand-wiring (Manual)

Comparative Telemetry

48.2k

Manual Tokens

19.4k

Auto Tokens

5 Stops

Human Interventions

+59.7%

Efficiency Gain

Tibo (Codex): "A true AI Agent shouldn't require users to maintain Skills, Memory, and Sub-agents themselves."

Execution Pipeline Trace ● Active Simulation

Step 6 of 6: Verification & Final Commit

Manual Configuration Paradigm Rigid Tools • Vector Memory

Autonomous Paradigm Zero-Config • Dynamic Synthesis

Telemetry & Bottleneck Diagnostics

Dimension	Manual Paradigm	Autonomous Paradigm	Architectural Impact
Cognitive Friction	High (42 mins setup/maintenance)	Zero (Direct Task Execution)	Developer focuses on intent rather than prompt stitching
Context Bloat	48,200 tokens (Unpruned schemas)	19,400 tokens (Dynamic compaction)	Reduced hallucination and context thrashing
Sub-Agent Dispatch	2 Bottlenecks (Static handover)	0 Bottlenecks (Ephemeral workers)	Seamless parallel execution without orchestrator locks

Assigned workload traces explain a token-overhead comparison

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The saved comparison uses six authored authentication-refactor steps. Manual base tokens sum forty eight thousand two hundred, and twenty four memory records add six hundred per step, or three thousand six hundred overall. That produces fifty one thousand eight hundred manual tokens. Autonomous assigned values sum nineteen thousand four hundred. At one pixel per two hundred tokens, the manual base, memory overhead and autonomous bars measure two hundred forty one, eighteen and ninety seven. The displayed reduction is about sixty two point five percent. This arithmetic follows fictional fixture values rather than a measured benchmark of real agent systems. Increasing memory from twenty four records to sixty four adds one thousand extra tokens per active step, so six steps add six thousand. Manual total becomes fifty seven thousand eight hundred while autonomous total stays nineteen thousand four hundred, changing the displayed reduction to about sixty six point four percent. At one pixel per two hundred tokens the two manual totals measure two hundred fifty nine and two hundred eighty nine. Turning dynamic synthesis off instead multiplies each autonomous fixture by one point five; their total becomes twenty nine thousand one hundred and the default-memory reduction about forty three point eight percent. These controls alter assigned assumptions without performing model inference or memory retrieval. The six default steps label five manual interventions and two manual bottlenecks. The displayed diagnostic assigns eight minutes per intervention, producing forty minutes. At ten pixels per count, the intervention and bottleneck bars measure fifty and twenty. Disabling hand-wired subagents hides those two bottleneck flags but does not remove assigned tokens or intervention counts. Step controls select prefixes of the stored scenario, and replay schedules that same prefix expansion rather than launching live workers. JSON export includes rendered token strings, workload and selected step count, not an actual provider usage ledger or executed test evidence. Native controls and export can work locally while the represented outcomes remain illustrative.