investigating-agentforce-architecture

investigating-agentforce-architecture — declared architecture snapshot

Design-time metadata tree for one Agentforce agent: planner → topics → actions → flows → Apex → prompts → NGA plugins. Reads declared metadata only — BotDefinition, GenAiPlanner, GenAiPlugin, GenAiFunction*, Flow, ApexClass, GenAiPromptTemplate. Does not read runtime audit rows.

Runtime budget: 30–45s typical, ≤60s hard cap on reference fixtures. Sequential baseline would be 90–220s; parallel Tooling SOQL fan-out delivers a 3–5× speedup. Large bots with many flows scale approximately linearly — each flow metadata retrieve is one round-trip.

Runs inline — no subagent. Every phase is deterministic file processing.

If the user hasn't given enough to proceed

When invoked with no agent_api_name AND no org alias, print the following block verbatim — do not paraphrase, do not pre-run any script. Trigger condition: $ARGUMENTS is empty OR names no agent (no --agent flag and no known agent API name in the prose) OR names no org (no --org flag and no known alias).

Which agent should I document, and in which org? I need: - Agent API name — the DeveloperName of the BotDefinition (e.g. MyAgent, MySalesAgent). Not the label. - Org alias — for sf CLI auth (the alias you configured with sf org login) Optional: - Version — an agent_version_api_name like v5. If omitted, I'll resolve the active BotVersion. - --force — ignore cached tree; re-fetch everything. - --reprobe — re-run the 7-day channel-probe cache (only needed after a Salesforce release). I'll run the metadata pipeline inline. Artifacts land under ~/.vibe/data/investigating-agentforce-architecture/<org_id15>/<agent_api_name>__<agent_version>/ (overridable with --data-dir).

Pipeline invocation

When the user has supplied --org <alias> + --agent <api_name> (plus any optional flags), run this block. One python3 invocation drives the full pipeline. main.py writes .emit_ctx.json; emit_result.py reads it and prints the final === RESULT === block last to stdout.

set -euo pipefail

# zsh arrays are 1-indexed by default; bash arrays are 0-indexed.
# This block uses 0-indexed semantics throughout (_args[$i] starting at i=0),
# so under zsh + `set -u` the very first read of `_args[0]` would trip
# `parameter not set`. KSH_ARRAYS makes zsh treat arrays as 0-indexed,
# matching the bash shebang's expectation. No-op under bash.
[ -n "${ZSH_VERSION:-}" ] && setopt KSH_ARRAYS

SKILL_ROOT="${SKILL_ROOT:-${PLUGIN_ROOT:-$HOME/.vibe/skills}/investigating-agentforce-architecture}"

# Argument parser. Accepts both `--org foo` and `--org=foo`.
# `$ARGUMENTS` is the raw user input Claude Code substitutes.
ARG_ORG=""
ARG_AGENT=""
ARG_VERSION=""
ARG_FORCE=""
ARG_REPROBE=""
ARG_PARALLELISM=""
ARG_MAX_MERMAID=""

# shellcheck disable=SC2206
_args=($ARGUMENTS)
i=0
while [ $i -lt ${#_args[@]} ]; do
 tok="${_args[$i]}"
 case "$tok" in
 --org=*) ARG_ORG="${tok#--org=}" ;;
 --org) i=$((i+1)); ARG_ORG="${_args[$i]:-}" ;;
 --agent=*) ARG_AGENT="${tok#--agent=}" ;;
 --agent) i=$((i+1)); ARG_AGENT="${_args[$i]:-}" ;;
 --version=*) ARG_VERSION="${tok#--version=}" ;;
 --version) i=$((i+1)); ARG_VERSION="${_args[$i]:-}" ;;
 --parallelism=*) ARG_PARALLELISM="${tok#--parallelism=}" ;;
 --parallelism) i=$((i+1)); ARG_PARALLELISM="${_args[$i]:-}" ;;
 --max-mermaid-nodes=*) ARG_MAX_MERMAID="${tok#--max-mermaid-nodes=}" ;;
 --max-mermaid-nodes) i=$((i+1)); ARG_MAX_MERMAID="${_args[$i]:-}" ;;
 --force) ARG_FORCE="1" ;;
 --reprobe) ARG_REPROBE="1" ;;
 esac
 i=$((i+1))
done

# Usage block if required flags missing. Agent reads stderr,
# prints verbatim, and stops — does NOT pre-run main.py.
if [ -z "$ARG_ORG" ] || [ -z "$ARG_AGENT" ]; then
 cat >&2 <<'USAGE'
> Which agent should I document, and in which org?
>
> I need:
> - **Agent API name** — the BotDefinition.DeveloperName (e.g. `MyAgent`)
> - **Org alias** — for `sf` CLI auth (the alias you configured with `sf org login`)
>
> Optional flags:
> - `--version v5` — pin a specific BotVersion (default: Active+highest)
> - `--force` — bypass cache
> - `--reprobe` — force channel-probe refresh
> - `--parallelism N` — ThreadPoolExecutor size (default 5)
> - `--max-mermaid-nodes N` — cap Mermaid node count (default 80)
USAGE
 exit 2
fi

# Fresh work dir per invocation. Epoch + random suffix avoids collisions
# between concurrent runs on the same host.
WORK_DIR="/tmp/investigating-agentforce-architecture-$(date +%s)-$RANDOM"
mkdir -p "$WORK_DIR"

# Input validation at the boundary, BEFORE any python3 call.
# fs_guard exits 1 and prints an INVALID_INPUT RESULT block on failure;
# `|| exit 1` is mandatory — bare calls silently continue past failures.
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_AGENT" agent_api_name api_name || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_ORG" org_alias not_empty || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$WORK_DIR" WORK_DIR symlink || exit 1
python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$WORK_DIR" WORK_DIR owned || exit 1
if [ -n "$ARG_VERSION" ]; then
 python3 "$SKILL_ROOT/scripts/_shared/fs_guard.py" "$ARG_VERSION" agent_version api_name || exit 1
fi

# Single python3 call drives all pipeline phases. main.py writes
# `.emit_ctx.json` into $WORK_DIR — emit_result.py then renders the
# RESULT block from that ctx. No subprocess-per-phase.
_main_args=(--org-alias "$ARG_ORG" --agent "$ARG_AGENT" --work-dir "$WORK_DIR")
[ -n "$ARG_VERSION" ] && _main_args+=(--version "$ARG_VERSION")
[ -n "$ARG_FORCE" ] && _main_args+=(--force)
[ -n "$ARG_REPROBE" ] && _main_args+=(--reprobe)
[ -n "$ARG_PARALLELISM" ] && _main_args+=(--parallelism "$ARG_PARALLELISM")
[ -n "$ARG_MAX_MERMAID" ] && _main_args+=(--max-mermaid-nodes "$ARG_MAX_MERMAID")

# main.py returns nonzero on terminal failures; we DON'T short-circuit —
# emit_result still publishes the failure RESULT block. `set -e` is
# temporarily relaxed around this single call.
set +e
python3 "$SKILL_ROOT/scripts/main.py" "${_main_args[@]}"
_rc=$?
set -e

# Final RESULT block is emit_result.py's stdout — MUST be the last thing
# stdout sees. emit_result exits 0 on render success; the bash harness
# propagates main.py's rc for the agent's exit status.
WORK_DIR="$WORK_DIR" python3 "$SKILL_ROOT/tools/emit_result.py"
exit "$_rc"

Inputs

Outputs

All artifacts under ~/.vibe/data/investigating-agentforce-architecture/<org_id15>/<agent_api_name>__<agent_version>/ (default; override with --data-dir <path>):

<agent>_<ver>_metadata_tree.json   primary artifact — normalized planner/topic/action/flow/apex/prompt/plugin tree
<agent>_<ver>_architecture.md      human-readable section-by-section rendering (H1 + 7 numbered sections, plus a conditional Dependency graph appendix). Mermaid diagrams are embedded inside the relevant sections (Action tree, Data flow, and Dependency graph)

Pipeline — inline, no subagent

resolve_bot.py        → BotDefinition + BotVersion + planner name lookup
retrieve_planner.py   → Metadata API zip retrieve for GenAiPlannerBundle (+ NGA plugins if present)
parallel_retrieve.py  → 6 parallel Tooling SOQL channels fan out from the planner id
                          (resolved by the `planner_definition_by_agent_chain` seed query):
                          - plugins_by_planner (GenAiPluginDefinition)
                          - planner_bundle_functions (GenAiPlannerFunctionDef join)
                          - functions_by_plugins (GenAiFunctionDefinition)
                          - planner_attrs_by_parent_ids (GenAiPlannerAttrDefinition)
                          - plugin_functions_by_plugin_ids (GenAiPluginFunctionDef join)
                          - plugin_instructions_by_plugin_ids (GenAiPluginInstructionDef)
parse_bundle.py       → parse retrieved XML into normalized node shapes
parse_wave.py         → BFS expansion: flow/apex/prompt refs discovered in nodes
                          → SOQL for Flow/Apex bodies (batched by id list)
                          → Metadata retrieve ONLY for GenAiPromptTemplate (+ NGA external plugins conditionally)
finalize.py           → merge waves into metadata_tree.json
render_architecture.py → <agent>_<ver>_architecture.md + Mermaid invocation graph (capped at --max-mermaid-nodes)

Channel strategy — SOQL-first.

• Tooling SOQL for every normalized tree node (planner, plugins, functions, plugin-functions, plugin-instructions, planner-functions, planner-attrs) — 6 parallel channels keyed on planner id, plus the planner_definition_by_agent_chain seed query that resolves the planner id from the agent chain.
• Data API SOQL for Flow (by id) and Apex (by id or name) bodies — batched.
• Metadata retrieve only for two cases: (a) GenAiPromptTemplate (prompt bodies aren't cleanly exposed via Tooling SOQL), and (b) NGA external plugins when the planner is Native Generative Agent shape (skipped for classic ReAct).

This is where the 3–5× speedup comes from. A naive implementation would retrieve everything via Metadata API zips sequentially; parallel Tooling SOQL covers ~80% of the tree in a single fan-out.

Planner shapes — classic ReAct vs NGA

The skill normalizes two planner families into a single tree shape:

The ID-prefix router in resolve_invocation_target.py distinguishes the two: NGA InvocationTargets that look like ids (01p… = ApexClass, 301… = Flow, etc.) get resolved via id-scoped SOQL; DeveloperName targets go through name-scoped SOQL. Unknown prefixes surface as _unresolved[] with reason="unknown-id-prefix:<prefix>" — never silently dropped.

Caching

• Tree cache: metadata_tree.json is reused unless --force is passed. Cache key includes the asset-hash of every .soql / .yaml / .mmd template bundled with the skill — bump a template, the cache busts automatically.
• Channel probe cache: 7-day TTL on the per-org sf sobject describe results that validate every field name the SOQL assets reference. A Salesforce quarterly release that renames / removes a field triggers status: PROBE_FAILED; --reprobe forces a refresh.

Prerequisites

Reference docs to load when needed

Do NOT load eagerly. Load when the user's question requires it:

• references/soql_fields.md — per-sObject field reference for the 13 sObjects this skill touches (2 Data API + 11 Tooling), with [mandatory] vs [optional] tags. Load when the user asks about a specific field, or when debugging an INVALID_FIELD SOQL error.
• references/contract.json — machine-readable schema for metadata_tree.json. Load when writing downstream tooling that consumes the tree.
• references/architecture_sections.md — section-by-section structure of the rendered <agent>_<ver>_architecture.md.

Invariants worth knowing upfront

• Pipeline is deterministic. Same (org, agent, version) + static org metadata → byte-identical <agent>_<ver>_metadata_tree.json and <agent>_<ver>_architecture.md. Only manifest timestamps drift across re-runs.
• Forward-only traversal. Every discovered ref goes forward from planner → children. No backward lookups.
• Partial results are surfaced, not silenced. Any unresolved reference lands in _unresolved[] with reason=.... STATUS=PARTIAL_OK if any channel failed; STATUS=OK only on a clean run.
• Cycle detection is per-branch. Same flow visited along its own ancestor chain emits _cycle_back_to:<path> instead of recursing. A defensive MAX_BFS_DEPTH=20 guard backs the per-branch ancestor set; real-world agents bottom out well before either limit fires. (Earlier docs claimed a hard cap of 5; that was the historical limit and was abandoned because shared utility flows like handleFlowFault tripped it on every nested tree — see config.MAX_BFS_DEPTH for the rationale.)
• Child ordering is alphabetical by api_name (case-insensitive). Topics come before non-topic plannerActions at the root level. Flow-actionCall order is NOT sorted — that's the flow author's execution sequence.