Build AI agents that plan, act, and run on schedule
yno.ai runs goal-directed agent loops that combine planning, tool use, and MCP-based connections to your systems. Pick a model per task, wire up the tools, and schedule recurring runs.
What this looks like in practice
Multi-step research and triage
Define a goal and a tool set. The agent plans the steps, calls tools through MCP or function calling, reads the results, and revises its plan as it works. Use this for inbound triage, lead research, alert investigation, or document review where each input needs different lookups before a decision is reached.
Scheduled operational runs
Set agents to run on a cron schedule or trigger. Each run pulls fresh data from connected systems, executes the workflow, posts results to your tools, and records the trace. Useful for daily reports, end-of-week reconciliations, queue processing, or any recurring task where the steps vary by input but the goal is fixed.
Multi-model orchestration per task
Route subtasks to the model that fits. Use a reasoning-heavy model for planning and decisions, a faster model for extraction or classification, and a coding-tuned model for tool argument generation. The agent keeps state across steps so each model only sees what it needs, which keeps cost and latency tied to the work done.
Why yno.ai for Agent Automation
- Agent loop with planning, tool calls, and self-correction across multiple steps until the goal is reached
- MCP and function-calling tools that connect to internal APIs, databases, and SaaS systems your agent needs
- Scheduled and triggered runs with per-run logs, traces, and replay so production behavior stays inspectable
Recommended models
Key concepts
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Frequently asked
- What's the difference between agent automation and traditional RPA?
- RPA records deterministic UI or API steps and replays them in a fixed order. Agent automation is goal-directed: the agent plans steps, calls tools, reads results, and adjusts. RPA is a good fit for stable, repetitive flows. Agents fit work where inputs vary, decisions are needed mid-flow, or the path isn't fully known up front. The two are complementary and often run side by side.
- Can yno.ai's agents call my internal APIs and databases?
- Yes. Agents reach external systems through MCP servers or function-calling tools you define. You can wrap internal HTTP APIs, databases, or SaaS endpoints, set the input schema, and expose them to the agent. The agent decides which tools to call based on the goal and the tool descriptions, and you control which tools are available to which agent.
- How do I schedule recurring AI agent runs?
- Each agent can be attached to a cron schedule or an external trigger. On each fire, the agent runs with its configured tool set and any input payload from the trigger. Runs are isolated, logged, and traceable, so you can review what happened on a given day, replay a failed run, or change the schedule without touching the agent definition.
- How reliable are autonomous agent workflows in production?
- Reliability depends on how the agent is scoped. Narrow goals, well-typed tools, and bounded step counts behave more predictably than open-ended ones. yno.ai records each step's input, tool call, and output, so you can audit decisions, set retry and timeout policies, and route uncertain cases to human review. Treat agent runs like any other production workload: monitor, version, and roll back when needed.