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Agentic Workflow Builder
Engineer risk-aware, evidence-based agentic lifecycles with structured guardrails and validation checkpoints.
$5.99
Agentic Workflow Builder
Example session with this skill installed
Design a workflow for an agent that researches technical documentation, summarizes API specs, and updates a local wiki. Include tools for web search and file writing. Risk tolerance is low.
- Read your context and instructions
- Compiled the agentic workflow builder
I have designed a structured agentic workflow for the technical documentation update task, focusing on source validation and least-privilege file access.
Agentic Workflow Engineering Plan
1. Workflow Architecture
Search → Source Validation → Summarization → Human Review → Wiki Update → Verification
2. Risk Assessment
| Risk | Trigger | Impact | Likelihood | Mitigation |
|---|---|---|---|---|
| Hallucinated Specs | Contradictory docs | Incorrect wiki data | Medium | Evidence-based validation against source URLs |
| Path Traversal | Malicious file name | Unauthorized file write | Low | Path sanitization and restricted directory scope |
| Prompt Injection | Unfiltered web content | Tool hijacking | Medium | Pre-processing of search results before summarization |
3. Safety Guardrails
- Allowed actions: Read-only web access, Write-only access to
/docs/wiki/directory. - Disallowed actions: Executing scripts, accessing
/etc/or system config files. - Human approval points: Required before any
file_writeoperation to the local wiki. - Stop conditions: Workflow terminates if API response status is 4xx/5xx on more than 3 attempts.
4. Implementation Steps
- Search Phase: Agent uses
web_searchto find official documentation. - Validation Phase: Agent extracts version numbers and dates to ensure document recency.
- Drafting Phase: Summary is generated with inline citations to source URLs.
- Approval Phase: User reviews the Markdown draft for technical accuracy.
- Execution Phase:
wiki_writertool commits the approved content to the specified path.
Next steps
- Define the specific regex for path sanitization in the
wiki_writertool. - Configure the
web_searchtool to prioritize specific domains (e.g., GitHub, ReadMe.io). - Set up the local directory structure for the wiki storage.
Connects securely to your tools. The creator never sees your data.
About this skill
Agentic systems often fail due to lack of structure, missing guardrails, or unmanaged tool risks. This skill provides a rigorous engineering framework to design, validate, and monitor agentic lifecycles before they reach production.
What it does
- Architect workflows by breaking complex goals into structured stages from planning to output.
- Map agent roles with specific responsibilities, inputs, and least-privilege permission sets.
- Perform risk audits identifying triggers for prompt injection, tool misuse, and data exposure.
- Define safety guardrails including human-in-the-loop checkpoints and explicit stop conditions.
- Design failure handling with detection methods and recovery paths for cascading errors.
How it works
- Supply your task requirements, agent roles, and available tools or data sources.
- The skill performs a multi-point analysis across twelve engineering domains, from scope to monitoring.
- Review the generated Engineering Plan, which includes risk tables and validation requirements.
- Use the provided QA checklist to verify the workflow meets safety and reliability standards.
Frameworks & tools
This skill is framework-agnostic, working with LangChain, AutoGen, CrewAI, PydanticAI, or custom orchestration layers. It focuses on the architectural logic and safety patterns rather than specific syntax.
Why this beats prompting it yourself
Generic prompts often overlook failure states and security risks in autonomous loops. This skill enforces a strict, evidence-based engineering lifecycle that treats agent outputs as hypotheses requiring validation.
Use cases
- Design multi-agent systems for sensitive data processing with human approval.
- Audit tool-use workflows to prevent unintended system access or data leaks.
- Create robust error-handling logic for long-running autonomous tasks.
- Build evidence-led validation pipelines for LLM-generated technical outputs.
Known limitations
This skill designs the architecture and safety logic but does not write the implementation code or execute the tasks. It relies entirely on provided tool and data source information.
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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