Autonomous Review Moderation Agent Architecture Plan
Design an autonomous agent tool-calling pipeline to filter, score, and moderate inbound e-commerce customer reviews.
Use this plan when deploying an autonomous agent workflow that inspects user-generated text and media reviews for compliance, spam, and policy violations before publication. It establishes tool definitions, decision logic, and human-in-the-loop escalation paths.
Role: Principal AI Workflow Architect specializing in autonomous retail trust and safety systems.
Context
- Target Platform: {{store_platform}}
- Review Ingestion Channel: {{review_source_channel}}
- Compliance Standards: {{trust_safety_policy}}
- Confidence Baseline: {{moderation_threshold_score}}
- Tool Registry: {{target_agent_tools}}
- Escalation Route: {{escalation_webhook_endpoint}}
Task
Develop an implementation plan for an autonomous agent workflow chain that consumes incoming reviews from {{review_source_channel}}, verifies authentic purchases via tool calls, runs safety and sentiment evaluations, and automatically executes publish, quarantine, or escalation actions on {{store_platform}}.
Method
- Map the trigger payload structure from {{review_source_channel}} to the primary agent orchestrator state.
- Define tool schemas for database lookups against {{store_platform}} order history to verify verified-buyer status.
- Specify the policy-evaluation reasoning loop using {{trust_safety_policy}} to detect profanity, PII, competitor mentions, and fake sentiment.
- Establish the scoring rubric against {{moderation_threshold_score}} to bifurcate automated execution from edge cases.
- Draft the sequential tool-calling chain for auto-approving compliant reviews directly to the storefront.
- Formulate fallback handling and quarantine payload formatting sent to {{escalation_webhook_endpoint}} for borderline scores.
- Detail telemetry logging steps to audit tool execution latency, agent confidence drift, and API error codes.
Constraints
- MUST define explicit JSON input/output schemas for each tool declared in {{target_agent_tools}}.
- MUST NOT allow the agent to publish unverified five-star or one-star spikes without an order lookup tool call.
- Every branch must produce a deterministic state transition record.
- Total response length must remain between 450 and 700 words.
Output format
1. Ingestion & Tool Schema Specification
Table of required tools, inputs, outputs, and timeouts.
2. Sequential Agent Decision Flow
Numbered execution logic with branching condition thresholds.
3. Failover & Human Review Policy
Escalation payload schema and quarantine trigger criteria.
4. Telemetry and Audit Protocol
Metric names, log properties, and drift monitoring cadence.
Self-review
- Are all 6 variables ({{store_platform}}, {{review_source_channel}}, {{trust_safety_policy}}, {{moderation_threshold_score}}, {{target_agent_tools}}, {{escalation_webhook_endpoint}}) correctly integrated?
- Are tool-calling interfaces concrete with strict typed parameters?
- Is the transition logic between autonomous publishing and manual escalation unambiguous?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.