One agent stack, five jobs: what runs Avakata's client pipeline without a team

Key takeaways

• Content agent: reads traffic and citation data, identifies pages losing citation share, drafts improvement copy, queues for human review. Runs three times per week — not daily, because daily produces noise.
• GEO audit agent: queries 20 target questions across ChatGPT, Perplexity, and Google AI Mode every Monday. Outputs a structured citation share report with deltas from the prior week.
• PPC management agent: monitors campaign performance against ROAS targets, adjusts bids within approved parameter ranges, flags anomalies for human review within four hours of detection.
• Support triage agent: reads incoming client messages, classifies by urgency and type, drafts responses for human review, routes complex issues directly to the human queue with context summary.
• Analytics agent: pulls weekly KPIs every Friday, compares to prior period and targets, generates a five-bullet summary with the two highest-leverage actions for the following week. The human reads it Monday morning.

Avakata is a one-person enterprise running 160+ AI agent specialists. The bottleneck is never the work — it is orchestration. These five agents handle the work. The human handles the decisions.

The design principle behind the stack

Every agent in the stack has a single job. It does not try to be a general assistant. The content agent does not also run analytics. The PPC agent does not draft copy. Specialization is the design choice that keeps the stack reliable — a general-purpose agent that does everything makes it harder to debug when something goes wrong, and something always goes wrong.

The other principle: every agent outputs to a human review queue before any client-facing action. Agents draft; humans approve. That is the governance layer.

Agent 1 — the content agent

Job: Identify pages losing citation share and draft improvements.

How it works: The content agent reads the weekly GEO audit report and cross-references it with traffic data from the analytics stack. It identifies the three to five pages with the largest gap between organic traffic and citation share — pages that rank but are not cited. For each, it drafts rewritten section openings (to lead with the answer), a new FAQ block with 2–4 questions, and a freshness date update.

Human task: Review the drafts, approve or edit, publish. Usually 30 minutes, three times per week.

Agent 2 — the GEO audit agent

Job: Measure citation share across AI engines every week.

How it works: Every Monday at 06:00, the GEO audit agent queries 20 target questions per client across ChatGPT, Perplexity, and Google AI Mode. It records cited/not cited, citation position, and competitor citations. It produces a structured Markdown report with the week's citation share, delta from prior week, and top three highest-opportunity queries — questions where a competitor is being cited but Avakata's content is close.

Human task: Read the Monday report. Flag any anomalies. Approve the content agent's priority queue for the week based on the data.

Agent 3 — the PPC management agent

Job: Maintain ROAS targets without daily human babysitting.

How it works: The PPC agent monitors campaign performance every four hours against pre-set ROAS floors and ceilings. Within approved bid adjustment parameters (set by the human operator at campaign setup), it raises or lowers bids automatically. When performance moves outside parameters — a campaign spending 3x normal with ROAS dropping below floor — it flags the human immediately with a diagnostic summary.

Human task: Set parameters at campaign launch. Review the weekly performance summary. Respond to anomaly flags. Average time: 45 minutes per week per campaign.

Agent 4 — the support triage agent

Job: Ensure no client message goes unanswered for more than two hours.

How it works: The support triage agent reads all incoming client messages across email and the client portal. It classifies each by urgency (critical, normal, low) and type (billing, strategy, technical, reporting). For normal and low-priority messages, it drafts a response and queues it for human review. For critical messages, it routes directly to the human queue with a one-paragraph context summary of the client relationship and the issue.

Human task: Review the draft response queue once in the morning and once in the afternoon. Approve or edit. Average time: 20 minutes twice per day.

Agent 5 — the analytics agent

Job: Produce a weekly performance summary the human can act on in five minutes.

How it works: Every Friday at 17:00, the analytics agent pulls KPIs across all client accounts: traffic, citations, ROAS, conversion rate, and revenue deltas. It compares to the prior week and to the monthly target. It produces a five-bullet summary with the single highest-leverage action for each client in the following week. The human reads it Monday morning before the GEO audit report arrives.

Human task: Read the summary. Set the week's priorities based on it. The entire Monday planning session takes 45 minutes.

What the human actually does

With five agents running the above, the human workload is: review, decide, and set parameters. The agents handle execution. The human handles judgment. Total human time at Avakata per week: approximately eight hours. The agents run the other 160.

This stack is not proprietary — most of the tooling is available. The hard part is the orchestration design and the governance layer. If you want to see the setup in detail, that is what Avakata builds for clients.

Frequently asked questions

What is an agentic enterprise?

An agentic enterprise is a business that uses AI agents to execute the majority of its operational work, with humans responsible for oversight, decisions, and parameter-setting rather than direct execution. Avakata is a single-person agentic enterprise running 160+ specialized agents across engineering, content, marketing, data, and support.

How do you prevent AI agents from making costly mistakes in client accounts?

Every agent in the Avakata stack operates within pre-approved parameters and outputs to a human review queue before any client-facing action. The PPC agent adjusts bids only within ranges set by the human at campaign launch. The content agent drafts but does not publish. The support agent drafts but does not send. Agents execute; humans approve.

What is the minimum viable agentic stack for a solo operator or small agency?

Start with three agents: a content agent (reads your analytics, drafts content updates), a reporting agent (pulls weekly KPIs, surfaces the one most important action), and a triage agent (routes inbound communications). Three agents, each with a single job, running on a weekly rhythm. Add specialization after you have 90 days of data on what each one is producing.