Agentic Workflow

A single LLM call is stateless and synchronous. You ask Claude a question, it responds, and you get your answer. If the answer requires external data — rankings, review counts, competitor metrics — you fetch all that data first, stuff it into the prompt as context, and send it at once. The LLM reasons over those static inputs and produces a static output in one turn. Done.

An agentic workflow is iterative and goal-oriented. You define a goal — "Audit NAP consistency for [business] across 20 directories and flag any mismatches" — and the agent executes it autonomously without asking you for all data upfront. Instead, it fetches from directory A, compares to canonical NAP, logs any mismatch. Then directory B. Then C. At each step, the agent makes real-time decisions based on prior results. If a directory is unreachable, it retries or skips and logs. If a mismatch is found, it investigates deeper (old listing? location ambiguity?). The workflow adapts to what it discovers. This responsiveness and autonomy is what separates agentic workflows from simple batch jobs or scheduled scripts.

A typical agentic workflow has four phases:

Phase 1: Data Fetch — The agent collects raw inputs. For a ranking audit, it might call the Local Pack API for keyword set A, the Maps API for the business location, the Organic SERP API for competitor context. It assembles a dataset.

Phase 2: Reasoning Loop — The agent analyzes the data, identifies patterns, and decides what to do next. Has this business's rank dropped more than 2 positions? Did a competitor enter the top 3? Is there a seasonal trend? The agent reasons over these questions, often calling a reasoning LLM step to synthesize.

Phase 3: Action Execution — Based on reasoning, the agent executes actions. Upload a new business photo to GBP. File a citation request. Request a review on Google. Call a tool to update listing content. Actions may be API calls or human-in-the-loop tasks (the agent prepares an action queue for a human to approve).

Phase 4: Verification & Reporting — The agent verifies that actions succeeded (rank changed? citation updated?), logs outcomes, and compiles a human-readable report. The human reads the summary, not raw data.

Manual SEO work is repetitive. Audit NAP, run a geogrid, pull reviews, compare to last month, draft email to client. Each step is decision-logic without creativity. A human operator can do this in 2 hours. An agent can do it in 2 minutes, 24/7, and never skip a step.

Agentic workflows scale this. An agency managing 50 clients can't afford to manually audit all 50 every week. But a weekly agentic workflow running on all 50 — pull rankings, flag changes >2 positions, summarize by client, queue alerts — costs nothing after setup. The agent doesn't get tired, doesn't skip businesses, doesn't make typos in the report.

For competitive intelligence, agentic workflows are especially strong. A workflow that runs every 2 hours — check top 5 competitors' local pack rankings across 20 keywords, compare to stored baseline, flag if any competitor gained 3+ positions, draft a brief — gives you near-real-time market intelligence. You see shifts before your clients do.

Agentic workflows depend on external tools — ranking data, citation audits, review pulls, geogrid scans all require API calls. Before MCP, integrating a tool meant custom code per-agent. MCP flips this: expose all 40 LocalSEOData endpoints as MCP tools once, and any MCP-compatible agent sees them automatically. A prompt like "pull local pack results, compare to last week, flag rank changes" automatically routes to the right APIs without hardcoded definitions.

A practical example: weekly NAP audit workflow. Input: business name and location. The agent calls Citation Audit API to fetch NAP across 20 directories, compares each to canonical NAP, scores severity, logs mismatches, generates a report, and queues corrections. Build this by defining the workflow as a prompt, connecting MCP tools, and invoking the agent. This workflow is now reusable — run it weekly on all clients, modify thresholds without code changes, extend with new checks by updating the prompt. Agentic workflows at scale almost always use MCP because it decouples tool definitions from specific agents, enabling one definition to work across Claude Desktop, OpenClaw, Hermes, and Perplexity.