# Infrastructure Friction Assessment

*Generated 2026-06-07 from captured coding-session data (Helix Forge session
memory), after the Detect-hardening pass ([AGENTIC-WP-0005]). First data-driven
assessment of where our agentic coding sessions spend effort on plumbing rather
than work.*

## Method & data quality

- **Corpus:** 72 sessions captured across Claude + Grok. A session-quality filter
  ([detect/quality.py]) drops health-checks, smoke-tests, and interrupted runs
  (mostly `llm-connect` *"Say hello in one word"*). **27 are real coding sessions.**
- **Caveat:** the 41 % that were filtered out had been mislabeled `abandoned` by
  the outcome heuristic and produced a *false-positive* "cross-flavor abandoned"
  pattern in the first catalog — now purged. Treat any pre-hardening finding with
  suspicion.
- **Key framing:** all 27 real sessions ended in `success`. So the friction here
  is **cost/efficiency, not failure** — sessions get there, but pay an avoidable
  tax to do it.

## The headline number

Across the 27 real sessions, tool-call activity breaks down as:

| Bucket | Share |
|--------|------:|
| shell (Bash / run_terminal) | 38.2 % |
| edit | 30.2 % |
| read | 12.9 % |
| **State Hub MCP** | **10.3 %** |
| **task-management plumbing** | **5.8 %** |
| **schema-loading (`ToolSearch`)** | **1.5 %** |
| other | 1.1 % |

**~17.6 % of all tool calls in real coding sessions are coordination plumbing
(hub + task + schema-loading), not touching the repo.** Per-session infra-overhead
share: median **11.7 %**, p90 **26.1 %**, max **43.3 %** — it concentrates badly.

## Ranked friction

### 1. State Hub call volume — *highest cost, addressable*
State Hub MCP is 10.3 % of all tool calls and dominates the worst sessions:

| Repo (one session) | total calls | State Hub calls | overhead share |
|--------------------|------:|------:|------:|
| vergabe-teilnahme | 570 | **231** | 43 % |
| activity-core | 488 | 98 | 23 % |
| flex-auth | 236 | 35 (+27 task) | 29 % |
| net-kingdom | 129 | 25 | 22 % |

Root cause: many **fine-grained** calls — per-task status updates, per-event
progress writes, repeated `get_domain_summary`. 231 hub calls in a single session
is coordination overhead, not work.

### 2. Schema-loading thrash (`ToolSearch`) — *low cost, near-zero-effort fix*
**106 `ToolSearch` calls across 22 of 27 sessions (81 %).** The State Hub MCP
tools are *deferred*, so nearly every session re-discovers and re-loads the same
tool schemas before it can call them. This is pure overhead with no work value —
and it is **exactly the CLI/MCP-interface friction hypothesized.**

### 3. Task-management plumbing — 5.8 %
`TaskUpdate` / `TaskCreate` / `todo_write` / `update_task_status`. Overlaps with
(1); much of it is redundant status churn within a session.

### 4. Tool thrash — *session-shape, watch only*
11 sessions hammer a single tool 80–230× (usually Bash or Edit). Less an infra
problem than a sign of missing higher-level tooling; low priority.

### 5. Budget overrun — 3 sessions
Token cost well above peers. Secondary; revisit once (1)–(2) are addressed.

## Recommendations

**The CLI/MCP-interface hypothesis is validated as a top-2 friction, not a minor
issue.** Two high-ROI moves:

- **A. A State Hub skill (highest ROI).** A skill (or a pre-loaded tool manifest)
  that (i) **front-loads the common hub tool schemas** so agents stop
  `ToolSearch`-ing for them — eliminates finding #2 almost entirely (81 % of
  sessions) — and (ii) **teaches batched writes** (sync N task statuses in one
  call, fewer progress events) to attack finding #1. Low effort, broad reach.
- **B. Coarser hub operations.** Add bulk endpoints / a single "sync workplan
  statuses" op so a session doesn't make 200+ individual hub calls. This is the
  structural fix behind the skill's guidance.
- **C. Measure the effect (Phase 4).** After A/B land, compare infra-overhead
  share on subsequent sessions against this baseline (median 11.7 %, p90 26.1 %).
  This is precisely what the Measure phase is for — the loop closes here.

## What this assessment still can't see

- **Why** a session was expensive at the *content* level (specific error
  messages, repeated failed approaches) — the digest captures tool histograms and
  prompt/response snippets but not error-body text. Mining tool-result bodies for
  recurring failure messages is the natural next extension if root-cause depth is
  needed.
- Grok/Codex are thin in the corpus (4 Grok, 0 Codex sessions), so cross-flavor
  friction claims are Claude-weighted for now.

[AGENTIC-WP-0005]: ../workplans/AGENTIC-WP-0005-detect-hardening.md
[detect/quality.py]: ../session_memory/detect/quality.py