Evidence the run may read
- Approved forecast version
- Actual results
- Driver and organizational mapping
production blueprint · finance
Forecast variance analysis agent blueprint.
Decompose actual-versus-forecast differences and prepare driver hypotheses with traceable supporting data.
Search intent: AI agent for forecast variance analysis
Published by a2a cloudProduct-source methodology →
01 · the contract
Start from a bounded job, not a blank chat box.
trigger
Actuals for a forecast period become available.
owner
finance owner
agent stops at
FP&A approves explanations before management distribution or forecast change.
KPI · decrease
unexplained forecast variance (percent). Measure material variance remaining without an approved explanation.
outputs
Artifacts the run must produce
- Variance bridge
- Driver-level explanations
- Questions for accountable owners
02 · topology
Small specialists. Named handoffs. One accountable decision.
Each stage produces an artifact another stage can inspect. The final node is a person, not an autonomous write to an external system.
- 01
intake
Validate and normalize the forecast variance analysis inputs.
- 02
variance calculator
Produce the variance bridge.
- 03
driver analyst
Produce the driver-level explanations.
- 04
FP&A reviewer
Challenge the forecast variance analysis result and prepare an approval packet.
- 05
finance owner
FP&A approves explanations before management distribution or forecast change.
intake→ validated input packet →variance-calculator
variance-calculator→ Variance bridge →driver-analyst
driver-analyst→ Driver-level explanations →fp-a-reviewer
fp-a-reviewer→ approval packet with evidence references →human-approver
03 · authority
Grant the run only what this case needs.
Source material is read-only. Drafts land in a case-specific output path. Tools may read or propose; the human gate owns the external write.
case inputs
workspace/finance/forecast-variance-analysis/inputs/**Read only the evidence attached to this workflow instance.
case outputs
workspace/finance/forecast-variance-analysis/outputs/**Write drafts and evidence artifacts without modifying source records.
approved tools
finance:forecast-variance-analysis:read-or-proposeInvoke only tools explicitly granted for this run; external writes remain gated.
04 · implementation
A private, bounded starting manifest.
The blueprint starts private, caps its DAG, disables replanning, and exposes no public endpoint. Add only the tools and data adapters this workflow has approved.
a2a.yamlsafe starting point
name: finance-forecast-variance-analysis
version: 0.1.0
entrypoint: agent:BlueprintAgent
expose:
public: false
composition:
planning: deterministic_dag
max_nodes: 6
max_parallel: 1
max_replans: 005 · acceptance test
Pass only with evidence.
- 01
Evidence traceability
Every material conclusion cites an input artifact or a scoped tool result from this run.
- 02
Every narrative reconciles to the quantified bridge and labels hypotheses awaiting owner confirmation.
Every narrative reconciles to the quantified bridge and labels hypotheses awaiting owner confirmation.
- 03
Approval boundary
The run stops at a proposal and records the human decision before any external side effect.
failure containment
Stop small. Preserve the evidence.
Forecast and actuals use different mappings, calendars, or currencies.
Containment: Return a partial result with unresolved items; do not broaden scope or perform an external write.
Operator: Attach the missing evidence, narrow the brief, or explicitly approve a new scoped run.
A required input or tool grant is unavailable.
Containment: Stop the affected branch and preserve completed artifacts in the case output workspace.
Operator: Grant only the missing resource or continue with that branch marked out of scope.
06 · proof
Sign the run facts. Keep money in the billing ledger.
Current platform receipts sign caller identity or classification, skill, bounded input evidence, verified grant IDs when present, outcome or result preview, and timing. Optional file, tool, artifact, handoff, evaluation, and review fields require separate instrumentation and are not populated by default. Price, fees, payouts, and later human approvals remain separate platform records.
The example uses only fields populated by the current platform sealing paths. It is illustrative, not a record of a real customer run.
ExecutionReceipt · selected fieldsEd25519 token
{
"receipt_id": "rcpt_01J...",
"schema_version": 1,
"agent_name": "finance-forecast-variance-analysis",
"caller": "user:workflow-owner",
"task_id": "case_forecast_variance_analysis",
"skill_name": "forecast_variance_analysis",
"input_hash": "4d7c...9a2f",
"grant_ids": [
"grt_case_inputs",
"grt_tool_propose"
],
"status": "ok",
"result_preview": "Output prepared: Variance bridge. Human decision remains separate.",
"elapsed_ms": 4218
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put the pattern to work
Start private. Scope the authority. Require the decision.
Deploy the workflow as a bounded internal agent, verify its outputs and the receipt fields actually emitted, then expand only the scopes your acceptance test proves it needs.