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Agentic Workflows in Aliveo AI

Aliveo AI runs on multi-step workflows where every step is self-sufficient: it plans what to do, generates the code, executes it against your connected data, validates the result, and packages insights for the next step. Agents sit above these steps to coordinate them—linking context across stages, enforcing your business logic from the Data Knowledge Graph, and sequencing the right analyses for the goal at hand.

Why Agentic Workflows

  • End-to-end automation from a single instruction—no manual SQL, dashboard switching, or glue code.
  • Consistent execution that follows the same plan → code → run → validate → summarize pattern inside each step.
  • Business-aware results powered by your semantic layer (KPIs, tags, campaign structure) and connected sources.
  • Reusable Agent Library workflows that deliver repeatable reports, investigations, and alerts across channels.

How Steps Work

  • Plan inline: Scope KPIs, entities, and timeframes using your graph and previous context.
  • Generate & run: Produce the needed queries or transformations, then execute on Google Ads, Meta, LinkedIn, warehouses, Sheets, or CSVs.
  • Validate automatically: Check coverage, anomalies, and logical consistency before handing outputs forward.
  • Summarize for handoff: Package tables, charts, and narratives so downstream steps can build on them.

What the Agents Do

  • Workflow Orchestrator — Chains steps, maintains memory across runs, and routes follow-ups to the right logic.
  • Channel-Specific Agents — Apply platform nuances (attribution, creative coverage, pacing) for Google, Meta, LinkedIn, Apple Search Ads, TikTok, and cross-channel views.
  • Agent Library Patterns — Ship ready-made flows (pacing, anomalies, RCA, campaign investigators) that you can schedule or customize.
  • Custom Agents — Let you design bespoke multi-step flows while still inheriting the same plan → code → execute → validate loop per step.

Typical Run

  1. Start an Automation Agent with a goal like “Find WoW ROAS drops and explain why.”
  2. Steps execute end-to-end: each one plans, codes, runs, validates, and produces outputs for the next.
  3. Context carries forward via the orchestrator—segments, KPIs, exclusions, and prior findings stay in play.
  4. Results ship as narratives, tables, and visuals you can export or schedule.
  5. Feedback loops (ratings, usage) refine future runs automatically.

Explore the Agent Library

  • Browse the Agent Library for pre-built workflows across monitoring, investigation, pacing, and creative coverage.
  • Use the step-wise builder to adapt library flows or craft your own, then schedule and share via Automation Agents.