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Agentic Control Plane

The data

Most claims about AI agents are estimates. The numbers on this page aren’t — each one comes from something we ran, captured, metered, or scanned ourselves. This is the canonical index: the number, what it means, how it was produced, when, and the write-up with the full detail. If you cite one (please do), cite the method with it.

Two ground rules we hold ourselves to: cost and traffic figures are from our own workspaces — dogfood, not customer data — and every number links to the post where the methodology is spelled out, including its limitations.

Tool surfaces (captured from live traffic)

Number What it is Method & date Source
76 tools Declared by one real Claude Code session (v2.1, Chrome extension + connectors available): 35 core harness, 21 browser control, 20 connectors Captured from live API request bodies — the tools array the harness sends on every call. 2026-07 Tool Surface Index · the argued posture
64 of 76 Tools declared but never invoked in that session — standing capability, not used capability Same capture, declaration vs invocation log. 2026-07 ACP for coding agents
17 tools Declared by OpenAI Codex CLI (v0.142) via the Responses API Same capture method. 2026-07 Tool Surface Index
+21 tools, mid-session One session’s surface grew by 21 tools partway through the day — deferred tools loaded, no prompt, no changelog Declaration diffing across requests in captured traffic. 2026-07 Which tools to deny out of the box

Cite this section: https://agenticcontrolplane.com/data#tool-surfaces

Metered cost (priced per call, at API rates)

Number What it is Method & date Source
285,814 tool calls Governed tool calls metered across 96 of our own workspaces Every call through the ACP gateway logged with model, tokens, and estimated cost What 285,000 agent tool calls actually cost (2026-04, updated 2026-07)
97% Share of total spend that is the orchestration loop (the model re-reading context to pick the next step), not the leaf work Every call tagged callKind: loop or leaf; spend split by tag. 2026-07 snapshot The loop tax
77% of spend, 30% of calls One frontier model’s share of the bill vs its share of call volume — roughly 467× the per-call cost of the cheapest model in the workload Per-call cost attribution across the same 285,814 calls The teardown
85% reads Share of sampled tool calls that are read operations (read_file, grep, cd, …) Tool-name classification over the ~10,200-call sample (2026-06) The teardown
2.7 seconds Average duration of an orchestration step (chat.completion) — the loop is the expensive part Metered latency on governed calls. 2026-07 The loop tax
$148.16 One full working day of Claude Code on a Max subscription, priced at API rates: 276 model calls, 1,697 tool calls Model traffic routed through the ACP cost proxy; each call priced at list rates while the subscription passes through untouched. 2026-06 ACP for coding agents · Claude Code cost tracking
100% loop tax, 72M tokens That $148 session’s spend was entirely loop — 72M tokens of context re-read at a 100% cache hit rate (the only reason it wasn’t ~10× more); the last 28 turns bought 10% of the output Turn-by-turn session X-ray on the same proxy data Claude Code cost tracking
90% on one step Share of our agent-builder’s model bill spent on a single step — deliberately, because per-call attribution let us route each step to the model it needs Per-step cost attribution on our own production agent. 2026-06 One step is 90% of our agent’s model bill

Cite this section: https://agenticcontrolplane.com/data#metered-cost

Model benchmark (agents, not leaderboards)

Number What it is Method & date Source
13 models, two ways Flagships from Anthropic, OpenAI, Google + open models (Llama, DeepSeek, Qwen, GLM), tested as isolated tool calls and as full agent loops Deterministic grading where possible, a 3-judge model panel for prose; agent runs scored on completion, 3 runs per scenario with spread reported; cost = live pricing × actual tokens. 2026-06 We benchmarked 13 models on real agent runs
0.83–0.95 vs 0.06–0.77 The whole field ties on isolated tool calls; the same models spread by more than 10× on completing a real agent loop Same benchmark, both test modes The benchmark
0.83 → 0.06 DeepSeek V3.2’s isolated score vs its agent-completion score — perfect calls in a vacuum, cannot drive a loop to a finish Same benchmark The benchmark

Cite this section: https://agenticcontrolplane.com/data#model-benchmark

Ecosystem scans (security research)

Number What it is Method & date Source
7,522 skills scanned Every skill on the ClawHub registry, statically analyzed: 4,931 findings across 746 skills, ~61% estimated false-positive rate after triage 40 regex patterns from published research (Snyk, Cisco, Kaspersky), run airgapped in Docker with --network=none; static analysis — a floor, not a ceiling I audited 7,522 AI agent skills (2026-03)
8,216 MCP servers Public MCP servers scanned for input-validation posture (7,840 tools) and classified by auth appropriateness Registry-wide static scans; methodology and caveats in each post Input validation · auth appropriateness (2026-03)

Cite this section: https://agenticcontrolplane.com/data#ecosystem-scans

Using these numbers

Cite freely with attribution — “per Agentic Control Plane’s metered data” plus a link is ideal. The canonical URL for this page is https://agenticcontrolplane.com/data, and each section above has a stable anchor (#tool-surfaces, #metered-cost, #model-benchmark, #ecosystem-scans) if you’re citing one group of numbers. Prefer linking the source post where you can, because each post carries the caveats that keep the number honest (sample sizes, dogfood-not-customer scope, static-analysis limits). If a number here disagrees with a post, the post is canonical and this page needs an update — tell us.

The captures and meters that produce this data run continuously. To point them at your own agents:

curl -sf https://agenticcontrolplane.com/install.sh | bash