Last month I needed to pull leads from a LinkedIn search, enrich them with company size, and fire off a personalized cold email sequence—all without writing a single line of code. I turned to Bard, Google’s conversational AI, hoping it could glue together the scraper, the data enricher, and the email sender into a repeatable workflow. What I got was a mixed bag: impressive language generation paired with frustrating gaps in reliability that forced me to rebuild parts manually.
By the end of this piece you’ll know exactly where Bard shines, where it trips, and how to decide whether to build the pipeline yourself or grab a pre‑made blueprint from the vault.
Why does Bard choke on multi‑step logic?
Bard excels at turning a plain‑English prompt into a coherent snippet of code or a draft email. Give it a clear, single‑task request—”write a Python script that reads a CSV and outputs JSON”—and it usually nails it on the first try. The trouble starts when you ask it to chain several of those snippets together.
I tried to have Bard generate a full workflow: first a scraper that pulls LinkedIn URLs, then a function that calls Clearbit for company data, finally a Mailchimp API call to add the enriched lead to a campaign. The model produced three separate code blocks, but it never linked them. It assumed each block would run in isolation, forgetting to pass variables between steps. The result was a set of orphaned functions that threw NameError when I tried to run them.
This isn’t just a token‑limit issue; it’s a design gap. Bard treats each request as a fresh conversation, so it doesn’t retain context across multiple turns unless you explicitly paste the prior output back in. For a workflow that needs to pass data from step A to step B to step C, you end up doing the plumbing yourself.
Many tutorials show Bard as a “one‑stop shop” for automation, suggesting you can describe an entire pipeline in one prompt and get a ready‑to‑run script. They gloss over the fact that the model’s output is stateless. In practice, you’ll spend more time stitching the pieces together than you would writing them from scratch if you had a low‑code tool like Make or Zapier.
The reality is that Bard is best used as a *component* generator, not an orchestrator. Treat it like a talented junior developer who can write a function but needs a senior engineer to wire the system together.
How to debug when this breaks
When the generated code fails, start by isolating each block. Run the scraper snippet alone and verify it returns the expected data. If it does, move to the enrichment function and feed it the scraper’s output manually. Only after each piece works in isolation should you attempt to combine them.
Use print statements or a simple logger to capture intermediate values. I found that adding a temporary “debug.json” file after each step saved me hours of guesswork. If Bard’s output includes a placeholder like “YOUR_API_KEY_HERE”, replace it with a real key before testing—otherwise you’ll get authentication errors that mask the real problem.
Concrete named example: a real prompt, tool, and cost
Here’s the exact prompt I used to generate the LinkedIn scraper:
Write a Python script that uses requests and BeautifulSoup to scrape the first 50 results from a LinkedIn search URL passed as a variable `search_url`. Extract each result’s name, profile URL, and current title. Return a list of dictionaries.
Bard returned a functional script in about eight seconds. I then asked it to add a function that calls the Clearbit Enrichment API with the company domain extracted from the profile URL. The second prompt:
Add a function `enrich_company(domain)` that calls https://company.clearbit.com/v2/companies/find?domain=DOMAIN with an API key stored in env var `CLEARBIT_KEY`. Return the enriched JSON or None on failure.
The combined script was still missing the loop that fed each profile’s domain into `enrich_company`. I wrote a three‑line `for` loop myself, taking less than a minute. The total time to get a working scraper‑enricher was roughly twelve minutes, compared to the twenty‑plus minutes I’d spend wrestling with a no‑code platform’s UI for the same task.
Cost-wise, Bard itself is free via the Google AI Studio interface (as of 2026). The only paid components were the Clearbit API ($99/mo for 50k requests) and a Mailchimp plan ($20/mo for up to 500 contacts). If you already subscribe to those services, the marginal cost of using Bard is zero.
Concrete love
What I genuinely love about Bard is its ability to turn a vague idea into syntactically correct code instantly. When I’m stuck on a tricky regex or unsure how to paginate an API response, a single prompt often gives me a working starting point. That speed cuts the “blank page” paralysis that plagues many solo operators.
Concrete gripe
My biggest gripe is the lack of persistent memory across sessions. Even if I start a new chat and paste the previous output, Bard sometimes rewrites variable names or drops imports, forcing me to re‑check the code. It feels like working with a teammate who forgets the last meeting’s notes every time you reconvene.
Price opinion
For the core AI component, $0/mo is fair—there’s no subscription fee to access Bard’s model through the public UI. If Google ever puts a paywall on heavy usage, I’d reconsider, but as of now the free tier is enough for solo work and light agency tasks.
If you want the deep cut on this, AI meeting tools coverage.
If you’d rather skip the build and deploy a working version in an afternoon, we’ve packaged this workflow as a blueprint at deepusecase.com/vault/ai-automation-blueprint.