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Scheduled agent workflows in production
3 Google Ads accounts · $110K+ managed search spend · Jan 2025 – Sep 2026
Enso Labs is an AI consultancy that helps independent and midsize agencies turn scattered AI experiments into governed, measurable client delivery. We start with a 10-day diagnostic, then build and run your first agentic workflow in production — not just a roadmap.
↺ Every correction feeds back into the context and the eval set — the system improves with use.
An AI-run paid-search and analytics practice we built and operate for a pharma agency.
See how the practice runs in the case example →
The same practice, across the agency’s pharma search accounts.
Same agency as K / 01. See the case example →
Our own operations run on the managed agents we build for clients.
The engagement ladder ends in managed agents like these. See the engagements →
An LLM pipeline over technical and market documents.
Read the AI Market Intelligence Platform case →
An AI Center of Excellence design for a pharma agency.
See governance by risk tier →
Facts from individual engagements and our own operations, 2025–2026. Client names withheld. Your results depend on your workflows and data. Visuals are schematic.
Most agencies already pay for AI tools. What they lack is the system around them — the rules, context, checks and owners that let AI touch real client work. Without it, AI stays a side project, and the costs show up anyway:
Models change every quarter. The durable asset is the operating system around them: the context your clients’ work depends on, the guardrails that keep it safe, the evals that prove it is right, and the people who own it.
Eight workflows we see pay back first in agency delivery. The diagnostic tells you which one to start with.
An agent watches the sources you name and drafts a cited weekly brief for each account team.
A metric dictionary per client, drafted commentary, and alerts when a number moves outside its normal range.
Live for a pharma agency: scheduled status reportingAutomated checks on links, tags, naming, specs and approvals before anything goes live.
Each claim is matched to its approved reference and routed to medical, legal or regulatory review with the evidence attached.
Prospect, category and competitor research assembled into a first-draft point of view for the new-business team.
Scheduled checks that catch broken tags, missing conversions and taxonomy drift across client properties.
Live for a pharma agency: daily ad-account health checks across 3 Google Ads accountsTracks how clients appear in AI-generated answers and which sources get cited, with a gap list to act on.
Structures incoming requests, flags missing information and routes the brief to the right team with context attached.
Agentic workflows only pay off when they run on the systems your agency and its clients already use. These are the integrations and builds we deliver.
Lead scoring, routing and nurture flows; campaign-to-opportunity attribution; agent actions that read and write CRM records behind a human approval gate.
The same scoring, routing, nurture and attribution on HubSpot — plus HubSpot-to-Salesforce migration support: signal and attribution requirements, data mapping.
Lead intelligence from qualification to routing to nurture to pipeline, plus reporting automation, campaign QA and brief intake.
GA4 → BigQuery → Looker foundations and tracking health checks — one source of truth across campaign, CRM and web data.
Daily monitoring and optimization across Google, LinkedIn and Meta. Budget follows performance continuously, not at the monthly review — with anomaly alerts and human approval on every change.
AI growth marketing →Ten business days from scattered experiments to a prioritized, governed plan — and a first agentic workflow ready to build.
Interviews with leadership and delivery leads; map current workflows, tools, data access and client approval paths.
Score candidate workflows on value, feasibility and risk; agree on the governance tier for each.
Blueprint the first agentic workflow: context, guardrails, evals, owners and the KPI it must move.
Executive readout with the phased roadmap, operating model and business case.
How we prioritize: four questions, in order
What you walk away with
Sample deliverable
Illustrative example of the format, not client data. Your matrix also scores risk tier.
A regulated claim and an internal competitor scan should not share the same approval path. We set controls by what is at stake, so high-risk work stays safe and low-risk work gets fast.
| Risk tier | Examples | Controls | Pace |
|---|---|---|---|
| High-stakes & regulated | Product claims, healthcare content, client-facing numbers | Human approval before release; every claim linked to its evidence | Accuracy first |
| Client-facing, standard | Performance commentary, social copy, status reports | Human review with evals on golden questions; sampled audits | Balanced |
| Internal research & ops | Competitive scans, pitch prep, brief structuring | Logged and spot-checked; the team edits, the agent learns | Speed first |
A polished demo is not the test. Judge any partner — including us — on these four dimensions.
Rules your delivery teams can actually use: what AI may touch, who approves, how client data is kept separate.
A partner should leave something running, not just a roadmap. Ask what is live when the engagement ends.
Tools do not change habits. Look for named owners, enablement tied to real work, and a feedback loop.
Every workflow ships with a baseline, a KPI and evals, so you can prove value to leadership and clients.
Best for: Agencies with scattered AI use and no agreed first workflow.
You getBest for: Teams with a prioritized workflow ready to build.
You getBest for: Agencies scaling AI across accounts and service lines.
You getRun paid search for multiple pharma brands across two manufacturers with daily rigor — while every change stays inside regulated review.
We built nine scheduled agent workflows for the agency’s search practice, and designed its AI Center of Excellence: deployment playbooks and AI governance aligned to NIST AI RMF and FDA/MLR/PRC review.
The practice runs on agents every day: health checks, keyword harvesting, approval queues, billing prep and status reporting across 3 Google Ads accounts and $110K+ in managed search spend (Jan 2025 – Sep 2026).
Scope of the practice · one pharma agency · schematic
“Where is AI actually saving us time — and where is it adding risk?”
“Get our weekly client reporting drafted and checked before the account lead opens it.”
“Roll the same workflow out to five more accounts without adding review burden.”
↺ Each cycle adds a workflow to your agency’s AI center of excellence.
Sav Banerjee brings 15+ years in agency brand, CX and data strategy, and is an Anthropic Claude Certified Architect (2026). Today we run 23 scheduled agent workflows in production across 37 GitHub repos — the same managed-agent practice we build for agencies.
Ten business days from kickoff to executive readout. If you continue, the pilot-to-production sprint typically runs four to eight weeks, depending on the workflow and data access.
An executive sponsor, a named owner for each candidate workflow, access to the relevant tools and sample work, and a weekly review slot. We do the interviews, mapping, scoring and design work.
We work inside your approved tools and accounts, keep each client’s data separate, use enterprise model settings that exclude your data from training where the platform offers it, and log what agents read and produce. Client names and data never appear in our materials.
Yes. Regulated work sits in the highest governance tier: every claim is linked to its approved evidence and nothing is released without human approval by your medical, legal or regulatory reviewers. AI speeds up preparation and checking; it does not replace review.
We are model- and platform-agnostic and start from the stack you already license. The deliverable is the workflow around the model — context, guardrails, evals and owners — so it keeps working as models change.
You get a go / no-go decision on a pilot-to-production sprint for the top workflow. Agencies can then move to an AI center of excellence retainer, where Enso operates managed agents and adds workflows from the roadmap. There is no obligation to continue.
It is for independent and midsize agencies and in-house marketing teams that already use AI tools informally and want a governed, measurable delivery capability. It is not for teams looking only for prompt training or a tool license, or without a leader willing to own a workflow.
For a pharma agency, we built and operate an AI-run paid-search and analytics practice: 9 scheduled agent workflows (daily ad-account health checks, keyword harvesting, approval queues, billing prep and status reporting) across 3 Google Ads accounts and multiple brands for 2 manufacturers, with $110K+ in managed search spend from January 2025 to September 2026. We also designed that agency’s AI Center of Excellence, with governance aligned to NIST AI RMF and FDA/MLR/PRC review. Enso Labs itself runs 23 scheduled agent workflows in production across 37 GitHub repos.
Training changes skills and audits produce recommendations. Enso ends the diagnostic with a build-ready workflow blueprint, then builds and runs that first workflow in production with you, measured against a baseline.
A short review of where AI sits in your agency today — and whether the 10-day diagnostic is the right next step.