.png)
Before you begin, make sure you have a Jeeya.ai account, access to any data sources you want the agent to use (APIs, databases, files), and API keys or credentials for those services.
Familiarize yourself with the pricing plan you’re on — some features (advanced connectors or higher concurrency) may require a paid tier. Start by logging into your Jeeya.ai dashboard and creating a new project. Give it a clear name that reflects the agent’s purpose (for example, “Support Super Agent” or “Sales Outreach Agent”) so it’s easy to manage as you add versions and integrations. Define the agent’s goal and scope. A “super agent” can do many things, but you should specify the primary tasks (answering product questions, performing transactions, scheduling, summarizing data, etc.). Write a brief spec: inputs it will receive, outputs it should produce, allowed actions (API calls, DB writes), and any constraints (security, rate limits, privacy). Configure data sources and integrations. In Jeeya.ai, add connectors for the services the agent needs: - Link APIs by entering base URLs and keys; test endpoints inside the connector UI. - Connect databases or vector stores for retrieval-augmented generation (RAG) — upload documents or set up continuous sync. - Enable webhooks or message channels (Slack, email, chat widget) if the agent will interact with users in real time. Design the agent’s architecture: pick the LLM and tooling stack. Choose the model that balances cost and latency for your use case (e.g., an efficient LLM for chat, a larger model for complex reasoning). Add tools the agent can call
— a calendar API, CRM write access, or custom Python/JS code actions.
In Jeeya.ai, register each tool and define its input/output schema so the agent can call them reliably. Craft effective prompts and instruction layers. Create a system prompt that sets behavior, tone, and safety rules. Build task-specific templates for common interactions (greeting + intent detection, information retrieval + summarize, and action confirmation). Use examples (few-shot) to guide the model toward consistent outputs and define fallback responses when it’s unsure. Set up retrieval and context management. If the agent relies on company knowledge, index documents into a vector store and tune retrieval parameters (top-k, similarity threshold). Configure context windows so the agent receives relevant history without exceeding token limits — prioritize recent and highly relevant results. Create workflows and decision logic. Use Jeeya.ai’s flow or orchestration features to map multi-step processes: detect intent → gather missing info → call tool → validate response → confirm with user. For transactional tasks, implement confirmation and audit logging to ensure actions are reversible or reviewed. Test thoroughly in a sandbox. Run scripted scenarios and edge cases: ambiguous queries, conflicting data, and failure of external services. Verify tool calls execute correctly and handle errors gracefully. Monitor latency, response quality, hallucination rates, and security concerns (PII exposure). Use automated test suites where possible to validate regressions after updates. Iterate on evaluation metrics. Define KPIs such as resolution rate, average handle time, user satisfaction, and tool success rate. Use user feedback to refine prompts, expand training examples, and adjust retrieval weighting. Periodically retrain or refresh indexed documents so the agent stays current. Deploy cautiously and enable observability. Roll out to a limited audience or specific channel first. Enable logging, usage dashboards, and alerts for high error rates or unusual behavior. Ensure you have a rollback plan and human-in-the-loop escalation for complex or risky actions. Harden security and compliance. Restrict tool permissions to least privilege, encrypt credentials, and mask or redact sensitive data in logs. Implement access controls in Jeeya.ai and in connected services, and ensure your use of data follows GDPR or relevant regulations. Best practices and tips: - Start small: implement a focused set of capabilities, prove value, then expand. - Keep prompts modular: separate system instructions, user-facing templates, and tool schemas. - Use structured outputs (JSON schemas) for tool responses to reduce parsing errors. - Monitor for drift: model behavior can change after updates; re-evaluate prompts and tests when models or data change. - Maintain changelogs and version control for prompts, tools, and connectors. Example quick checklist: - Account and project created - Data connectors added and tested - Model and tools configured - Prompts and templates prepared - Workflows orchestrated and tested - Sandbox validation passed - Observability and security enabled - Gradual deployment plan in place By following these steps, you’ll have a robust, reliable AI super agent in Jeeya.ai that can handle complex tasks, integrate with your systems, and scale safely. If you want, tell me what your agent should do (support, sales, internal assistant, etc.) and I’ll provide a tailored prompt template and connector list to get you started. Subscribe to get hands-on templates, prompt libraries, and update alerts for new Jeeya.ai features.
.png)