Prospect Audit & Lead Scoring
One prompt triggers five parallel agents that research companies, find decision-makers, qualify leads.
What it does
Get a full prospect audit with researched companies, identified decision-makers, qualified leads, and personalized outreach sequences in under one minute by simply entering your target details.
Step by step
- Set up five concurrent sub-agents using Claude Code's parallel execution capabilities to handle distinct tasks like company research, decision-maker identification, and lead qualification simultaneously.
- Define a strict JSON schema for the output of each agent to ensure consistent data structures before aggregation.
- Implement a final orchestration step that aggregates the results from all five agents into a single unified prospect profile.
Where the LLM does the work
- Generate personalized outreach sequences by analyzing the aggregated prospect data and tailoring the tone and content to match the specific industry and role of the decision-maker.
- Determine lead scoring criteria dynamically based on the qualitative signals found during the research phase, such as recent funding news or hiring trends.
Watch out for
Parallel agents may exceed token limits if company research returns overly verbose data so implement strict summarization steps before aggregation.
Decision-maker identification can yield false positives if relying solely on public profiles so verify roles against current organizational charts where possible.
Personalized outreach might sound generic if the LLM fails to incorporate specific recent news about the target company so include a mandatory context injection step.
Tools that fit
Claude Code LLM Orchestrating parallel agents for research and outreach
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Prospect Audit & Lead Scoring: One prompt triggers five parallel agents that research companies, find decision-makers, qualify leads. ## Specification - What it does: Get a full prospect audit with researched companies, identified decision-makers, qualified leads, and personalized outreach sequences in under one minute by simply entering your target details. - Trigger: Run manually (Manual · on demand) - Autonomy: You stay in control - Expected setup effort: about an afternoon - Tools/services involved: - Claude Code: Orchestrating parallel agents for research and outreach ## Known pitfalls, handle each one explicitly in your implementation 1. Parallel agents may exceed token limits if company research returns overly verbose data so implement strict summarization steps before aggregation. 2. Decision-maker identification can yield false positives if relying solely on public profiles so verify roles against current organizational charts where possible. 3. Personalized outreach might sound generic if the LLM fails to incorporate specific recent news about the target company so include a mandatory context injection step. ## Reference implementation https://www.youtube.com/watch?v=JqXkPlX_4gQ (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-prospect-audit-lead-scoring/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than Anthropic?
Yes. Anthropic is only the example LLM in this recipe. The same flow works with Cohere, DeepSeek, Google Gemini and Google Vertex AI. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for founders and execs who need sales handled without hiring for it.
Sales teams can plug this into their own CRM and call cadence just as easily.