Phase 3: The Intelligence Core – Agentic Workflows (Tasks 36–65)

With the data ingested and structured, we build the "brain." This phase utilizes CrewAI to create a team of specialized AI agents that mimic a human recruiting team: a Researcher (Scout), an Analyst (Matchmaker), and a Copywriter (Outreach).

3.1 Orchestration Framework Setup

Task 36: Initialize CrewAI Environment

  • SMART Objective: Configure the CrewAI runtime environment by Week 7.
  • Configuration: Define the agents.yaml and tasks.yaml files.
  • Framework Choice: CrewAI is selected for its ability to define "Personas". A "Senior Recruiter" persona performs better at evaluating resumes than a generic LLM.

Task 37: Develop "The Scout" Agent (Researcher)

  • Role: Market Researcher.
  • Goal: "Find the top 20 new opportunities today that match the User's Vector."
  • Tools: VectorSearchTool (queries Weaviate), ExaSearchTool (queries the web).
  • Logic: The Scout filters the raw stream. It creates a shortlist.

Task 38: Develop "The Matchmaker" Agent (Analyst)

  • Role: Career Coach / Venture Associate.
  • Goal: "Rigorously evaluate the shortlist. Calculate a Match Score (0-100) based on the user's hard constraints and soft preferences."
  • Chain of Thought: "The user wants a remote job. This job is remote. The user knows React. This job needs Vue. Score penalty: -10. Final Score: 85."

Task 39: Develop "The Networker" Agent (Outreach)

  • Role: PR Specialist.
  • Goal: "Draft the initial communication for the approved matches."
  • Capabilities: Must be able to switch tone—formal for a bank job, casual for a crypto bounty, passionate for a co-founder intro.

3.2 specialized Workflows (LangGraph)

For complex, multi-step processes where the agent might need to "go back" or handle errors, LangGraph is the superior tool.
Task 40: Job Application State Machine

  • SMART Objective: Map the application lifecycle.
  • States: New -> Researched -> Drafted -> User_Approved -> Applied -> FollowUp_Scheduled.
  • Error Handling: If the "Apply" step fails (e.g., form error), the state reverts to Error_Review for human intervention.

Task 41: Co-Founder Dating Workflow

  • SMART Objective: Manage the delicate "warm intro" process.
  • Logic:
    • Step 1: Check user's LinkedIn connections for mutuals.
    • Step 2: If Mutuals > 0, draft an "Ask for Intro" message to the connection.
    • Step 3: If Mutuals = 0, draft a cold message referencing a specific detail in the target's profile ("I saw your talk at PyCon...").

Task 42: Bounty Hunter Workflow

  • SMART Objective: Real-time reaction.
  • Logic:
    • Event: New Bounty Detected via RSS.
    • Check: Does user have required skills?
    • Action: If Match > 90%, send immediate Telegram push notification. (Bounties are time-sensitive).

3.3 Advanced Matching and Filtering Logic

Task 43: "North Star" Alignment Scoring

  • SMART Objective: Implement mission-based matching.
  • Technique: Calculate the semantic distance between the User’s "Manifesto" (a text blob describing their values) and the Company’s "Mission Statement."

Task 44: "Anti-Goal" Filtering

  • SMART Objective: Filter out deal-breakers.
  • Logic: Hard filters for industries (e.g., "Gambling," "Defense") or keywords ("Legacy Code," "On-call").

Task 45: Tech Stack Compatibility Matrix

  • SMART Objective: Granular skill matching.
  • Logic: Differentiate between "Required" and "Nice to have."
    • User has React, Job wants React -> +20 points.
    • User has React, Job wants Angular -> -5 points (transferable skill).
    • User has React, Job wants C++ -> -50 points (mismatch).

Task 46: Experience Calibration (Inflation/Deflation)

  • SMART Objective: Normalize titles.
  • Insight: A "VP" at a 5-person startup is equivalent to a "Senior" at Google. The agent must calibrate titles based on company size data (fetched via Firecrawl/Apify).

Task 47: Founder "Psychometric" Profiling

  • SMART Objective: Analyze co-founder bios for red flags.
  • Implementation: LLM analysis of bios. Flags: "Vague about equity," "History of failed ventures," "Aggressive language."

3.4 LLM Integration and Optimization

Task 48: LLM Selection (OpenAI vs Claude)

  • Decision: Use Claude 3.5 Sonnet for the "Networker" agent (better nuance/writing) and GPT-4o for the "Matchmaker" (better reasoning/json-mode).

Task 49: Semantic Caching

  • SMART Objective: Reduce API costs by 30%.
  • Implementation: Use GPTCache. If the agent analyzes the same job description twice (e.g., from two different boards), return the cached analysis.

Task 50: Fine-Tuning "The Coach" (Optional)

  • Objective: If base models fail to capture the user's voice, fine-tune a Llama-3-8B model on the user's past emails and cover letters.

3.5 Autonomous Action Execution

Task 51: Resume Customization Engine

  • SMART Objective: Generate a tailored resume for every application.
  • Implementation: The agent maintains a "Master Resume" JSON. It selects the relevant projects/bullets for the specific job and renders a new PDF using a LaTeX template.

Task 52: Cover Letter Generator

  • Technique: "One-Shot" prompting. "Here is the job. Here is the user's writing style. Write a cover letter that mentions [Company News X]."

Task 53: LinkedIn Connection Request Personalizer

  • Constraint: 300 characters max.
  • Logic: "Hi [Name], I saw you're building [Product]. I'm a dev dealing with [Problem] and would love to connect."

Task 54: Proposal Generator for Upwork

  • Logic: Address the client's problem in the first line. "I see you need a Python script to scrape YC. I have a Firecrawl setup ready to do this..."

Task 55: Calendar Scheduling Agent

  • Objective: Coordinate meetings.
  • Integration: Google Calendar API. When a positive reply is detected, the agent sends a Calendly link or proposes times.

Task 56: "Form Filler" Scripts (Selenium)

  • SMART Objective: Automate Greenhouse/Lever forms.
  • Implementation: Maintain a library of Selenium scripts for the top 5 ATS platforms. These have predictable DOMs (id="first_name").

Task 57: CAPTCHA Solving Integration

  • Tool: 2Captcha or CapSolver API.
  • Logic: If CAPTCHA detected -> Pause -> Send to API -> Wait for Token -> Inject Token.

Task 58: Cold Email Infrastructure

  • SMART Objective: Ensure deliverability.
  • Implementation: Use a dedicated subdomain for agentic outreach to protect the user's main domain reputation.

Task 59: Follow-Up Management

  • Logic: If no reply in 3 days -> Send polite bump. Max 2 follow-ups.

Task 60: Interview Prep Agent

  • Output: A "Dossier" PDF. Contains: Interviewer bios, recent company news, potential culture questions, and suggested questions to ask.

Task 61: Negotiation Advisor

  • Logic: When an offer is received, the agent searches levels.fyi for comparable salaries and suggests a counter-offer range.

Task 62: Portfolio "Project" Generator

  • Logic: For gig work, auto-select the 3 most relevant portfolio items to attach to the bid.

Task 63: Reference Checker

  • Logic: For potential co-founders, the agent searches for "Back-channel" references—people in the user's network who overlap with the target's past companies.

Task 64: "Stealth" Mode Operations

  • Logic: Ensure all LinkedIn views are done in "Private Mode" (if possible) or via the API to prevent "XYZ viewed your profile" notifications revealing the user.

Task 65: Error Handling and Retry Logic

  • Implementation: Dead Letter Queue. If an application fails, log it, alert the user, and retry later.