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Grok Bot+18 agentsAgent Architecture Blueprint
Production-grade workflow-to-agent architecture skill that converts messy human workflows into scoped, governable, cost-aware agent system blueprints.
$12
Agent Architecture Blueprint
Example session with this skill installed
Use @workflow-to-agent-architecture-blueprinter to design a system for triaging GitHub issues, labeling them, and drafting responses. Focus on preventing duplicate work and high token costs.
- Read your context and instructions
- Compiled the agent architecture blueprint
I have analyzed your GitHub triage workflow. Below is the production-grade architecture blueprint focused on cost discipline and duplicate suppression.
GitHub Triage Agent Architecture
0. Build Recommendation
- Status: Build now (Pilot first)
Justification: High volume of low-complexity triage makes this ideal for a tiered model approach. Potential to save 12+ hours/week for maintainers.
1. Failure Mode
- Prevents: Multiple agents responding to the same issue, labeling "bug" on feature requests, and burning high-tier tokens on simple spam filtering.
6. Agent Topology
| Agent Name | Role | Model Tier | Decisions Allowed |
|---|---|---|---|
| Filter-Bot | Noise reduction | Small (e.g., Haiku) | Categorize as Spam/Invalid/Valid |
| Label-Master | Categorization | Medium (e.g., Sonnet) | Assign labels, link to existing PRs |
| Draft-Smith | Content Gen | Medium (e.g., Sonnet) | Propose response text |
7. Tool Permissions Matrix
| Agent | Tool | Type | Approval |
|---|---|---|---|
| Filter-Bot | gh issue list | Read | No |
| Label-Master | gh issue edit | Write | No |
| Draft-Smith | gh issue comment | Write | Yes |
8. Memory Architecture
- Working Memory: Current issue thread and related PR links.
- Session Memory: Recently processed Issue IDs to prevent race conditions.
- Source-of-Truth: GitHub API (never cache issue status longer than 60 seconds).
10. Human-in-the-Loop Design
- Checkpoint: All
gh issue commentactions are held in apending_reviewstate. - Action: Maintainer views draft and clicks "Post" or "Edit".
13. Success Rubric
- Accuracy: >90% correct labeling vs manual history.
- Cost: <$0.02 per triaged issue via tiered routing.
- Safety: Zero automated comments posted without human sign-off.
Next steps
- Connect GitHub repository to the agent runtime.
- Define the "Spam vs Valid" classification criteria for the Relevance Layer.
- Deploy the Filter-Bot on a 5-minute cron trigger for initial pilot.
Connects securely to your tools. The creator never sees your data.
About this skill
Stop building fragile, monolithic agent prompts that break at the first sign of edge cases. Most agentic systems fail because they lack clear boundaries, state management, and failure-mode analysis. This skill converts messy, multi-step human workflows into production-grade architecture blueprints designed for governance and reliability.
What it does
- Failure-mode mapping identifies specific operational risks to eliminate before designing logic.
- Agent topology design defines narrow responsibilities, explicit handoffs, and scoped decision boundaries.
- Layered architecture builds relevance filters, triggers, memory structures, and guardrails into every plan.
- Token cost discipline routes tasks to specific model tiers based on complexity rather than defaulting to the most expensive model.
- Human-in-the-loop logic embeds mandatory approval gates at high-consequence or regulated boundaries.
How it works
- Intake Analyze the manual workflow, systems involved, and primary failure points.
- Evaluation Determine if agentization is justified or if a deterministic script is a better fit.
- Blueprinting Construct a multi-layered design covering relevance, triggers, workflow, agents, tools, memory, and governance.
- Stress testing Simulate failures like duplicate triggers, tool outages, and prompt injection to harden the design.
Frameworks & tools
This skill is compatible with filesystem-based agent runtimes including Claude Code, Cursor, Windsurf, and Roo. It generates blueprints suitable for implementation in LangGraph, OpenAI Agents SDK, or custom orchestration layers.
Why this beats prompting it yourself
Generic prompts often result in "agent sprawl" where one model tries to do everything, leading to hallucinations and high costs. This skill enforces a boris-cherny-style depth that separates deterministic steps from agentic discretion, ensuring your system remains governable and cost-effective.
Use cases
- Designing a lead intake system that qualifies data through CRM and email connectors.
- Architecting a multi-agent PR review and code quality pipeline.
- Mapping a customer support escalation bot with strict tool permissions and human-in-the-loop checkpoints.
Known limitations
Does not write the final implementation code; provides the architectural blueprint only. Requires explicit user consent before suggesting or using external connectors.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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