Titan Ventures: Multi-Agent System for Heuristic Venture Capital Due Diligence

1. System Purpose (Academic Framing)

Titan Ventures is a Multi-Agent System (MAS) built on the CrewAI framework, designed to automate the complex, multi-dimensional process of venture capital due diligence. In institutional investing, decision-making is often hampered by cognitive biases and information silos. This system utilizes Information Decomposition to break down a high-stakes investment query into specialized domains.

By employing a Multi-Stage Reasoning System, Titan Ventures simulates a professional investment committee. This approach allows for:

• Separation of Concerns: Each agent operates within a strict heuristic boundary, preventing "analysis bleed" where optimism about a market might prematurely cloud a technical audit.
• Cognitive Load Distribution: By decomposing the startup evaluation into Market, Tech, and Risk vectors, the system achieves higher granularity than a single-prompt LLM approach.
• Structured Heuristics: The system moves beyond "vibe-based" text generation into a rigorous, evidence-based decision pipeline.

2. System Architecture

The system is built on a Sequential Pipeline Design (1 → 2 → 3 → 4).

Why Sequential?

While hierarchical models (Manager-Worker) offer flexibility, the Sequential Process was selected to enforce a Logical Dependency Chain. In venture capital, a technical audit is only relevant within the context of the identified market opportunity, and a risk profile is only meaningful once the technical feasibility is established. This architecture creates a transparent, auditable Information Lineage where every decision is grounded in the refined data of the previous stage.

3. Agent Design & Reasoning Logic

| Agent | Role | Organizational Responsibility | Reasoning Style | Tools | | :--- | :--- | :--- | :--- | :--- | | Market Research Manager | Intelligence Gathering | External environment & TAM/SAM validation | Inductive / Exploratory: Synthesizing broad industry patterns into a market thesis. | Serper.dev | | Technology Evaluation Manager | Technical Auditor | Product defensibility & IP feasibility | Deductive / Validation: Testing product claims against known technical constraints. | None | | Risk Assessment Manager | Quantitative Risk Officer | Defensive capital protection | Adversarial / Stress-Testing: Identifying "Black Swan" events and execution bottlenecks. | None | | Investment Committee Manager | Principal Decision Maker | Final synthesis & verdict | Synthetic / Integrative: Weighing conflicting specialist signals to form conviction. | None |

4. Agent Collaboration & Information Flow

Titan Ventures utilizes a Corporate Information Relay Model. Information is not merely passed; it is transformed at each node:

1. Task 1 → Task 2: Raw startup data is broken down into market-specific queries. The Market data is passed to the Tech Auditor, who filters technical potential through the lens of market demand.
2. Task 2 → Task 3: Technical strengths are passed to the Risk Manager, who identifies "Negative Intelligence"—the reasons not to invest—acting as an adversarial filter.
3. Task 3 → Task 4: The Investment Committee receives the culmination of these signals to perform final Information Integration, resolving contradictions between the specialist agents.

5. Traceability Matrix (Data Lineage)

| Final Decision Element | Source Agent | Data Lineage / Transformation | | :--- | :--- | :--- | | Investment Rationale | Agent 1 (Market) | Based on industry trends and competitor gaps identified via Serper.dev in Step 1. | | Technical Moat | Agent 2 (Tech) | Validated against the market feasibility report provided by Agent 1. | | Risk Mitigation Plan | Agent 3 (Risk) | Derived from the technical weaknesses identified by Agent 2. | | Final Recommendation | Agent 4 (IC) | A weighted synthesis of Market Upside vs. Risk Severity. |

6. Decision Logic & Veto Rules

To ensure deterministic quality and remove "vibe-based" decision making, the system follows a structured Veto Framework:

• The Veto Rule: If the Risk Assessment Manager (Agent 3) identifies a "Fatal Flaw" (e.g., lack of IP defensibility or an unfixable burn rate), the Investment Committee is instructed to issue an Automatic Pass, regardless of market size.
• Quantitative Scoring (Titan Score): Agents provide a 1-10 score for their vector.
  • Score > 8.0: High Conviction (Invest).
  • Score 5.0 - 7.9: Neutral / Conditional Invest (Requires further DD).
  • Score < 5.0: Automatic Downgrade (Pass).

7. Execution Status & Evidence

Execution Status

The Titan Ventures multi-agent workflow has successfully completed an end-to-end execution. The CrewAI sequential orchestration processed the input (NexGen Quantum) and successfully generated a Final Investment Decision Memo.

Execution Results Summary:

• Final Recommendation: INVEST
• Titan Score: 8.0
• Workflow: Completed end-to-end using CrewAI sequential orchestration.
• LLM Provider: Google Gemini 2.5 Flash.
• External Integration: Serper was successfully utilized for market research web searches.

Execution Timeline

1. Market Research: The Market Research Manager queried external sources via Serper to establish the initial market context.
2. Technical Audit: The Technology Evaluation Manager assessed the technical feasibility based on the market summary.
3. Risk Analysis: The Risk Assessment Manager evaluated potential risks, generating a negative intelligence filter.
4. Final Decision: The Investment Committee Manager synthesized the previous reports into a final "INVEST" recommendation with a Titan Score of 8.0.

Execution Evidence

The successful execution artifacts are preserved in the repository:

• docs/checkpoints/market_research.md
• docs/checkpoints/technology_evaluation.md
• docs/checkpoints/risk_assessment.md
• docs/checkpoints/final_investment_decision.md
• docs/execution_evidence/token_usage_report.md

All execution evidence is fully reproducible by running:

python main.py

Token Efficiency Results

Token optimization mechanisms were successfully enabled during execution:

• Configured max_iter=2, verbose=False, and allow_delegation=False.
• Summary-only context propagation drastically reduced the context window size between agent handoffs.
• This optimization significantly lowered API costs and reduced the risk of hitting Gemini API rate limits, compared to passing full, uncompressed historical task outputs.

Checkpoint and Resume Validation

The checkpoint and resume functionality worked correctly during execution. Completed tasks are automatically saved as markdown files. On subsequent runs, if a checkpoint exists in docs/checkpoints/, the system automatically loads the saved content and skips the agent execution, saving API quota and time.

Limitations & Future Work

• Google Gemini API Availability: The external Google Gemini service may occasionally return HTTP 503 UNAVAILABLE errors during periods of high demand. This is an external service limitation, and rerunning the workflow (utilizing the built-in checkpointing) usually resolves the issue.
• Future Work: Future iterations of Titan Ventures could include the integration of financial document parsing tools (e.g., PDF readers for pitch decks) and a hierarchical execution process to allow the Investment Committee Manager to actively query specialists for more information if the initial reports are insufficient.

8. Project Documentation Links

To understand the full scope of the project, please refer to the following documents:

Interactive Execution Report

• View Interactive Execution Report
  This self-contained HTML report provides an auditable, evidence-based overview of the workflow execution, allowing reviewers to inspect the generated outputs directly.

Additional Documentation

• Product Requirements Document (PRD)
• Project Plan (PLAN)
• Project Tasks (TODO)
• Token Usage Report

9. Setup & Execution Instructions

Prerequisites

• Python 3.10+
• Google Gemini API Key
• Serper.dev API Key (for market research)

Installation

1. Clone the Repository:

   git clone <repo_url>
   cd titan_ventures
   

2. Install Dependencies:

   pip install -r requirements.txt
   

Environment Configuration

The system is powered by Google Gemini 2.5 Flash as its primary LLM. Create a local .env file in the root directory based on the provided .env.example:

GEMINI_API_KEY=your_gemini_api_key_here
SERPER_API_KEY=your_serper_api_key_here

Execution

Run the main evaluation pipeline directly from the terminal:

python main.py

10. Security & Prompt Injection Awareness

• API Key Protection: Never commit API keys. The .env file must remain local to your machine and is explicitly ignored in the .gitignore file. Only the .env.example template should be shared.
• Prompt Injection Awareness: To mitigate prompt injection risks from untrusted startup inputs, agents operate within rigid persona constraints (allow_delegation=False). Downstream agents only receive structured summaries rather than raw user inputs or raw search strings. The Risk Assessment Manager provides an adversarial check against hallucinated or manipulated data.

11. Visual Architecture

flowchart TD
    Input[Startup Raw Data] --> A1[Market Research Manager]
    subgraph Analysis_Phase
    A1 -- "Market Context" --> A2[Tech Evaluation Manager]
    A2 -- "Technical Feasibility" --> A3[Risk Assessment Manager]
    end
    subgraph Decision_Phase
    A3 -- "Risk Profile" --> A4[Investment Committee Manager]
    A1 -. "Direct Context" .-> A4
    A2 -. "Direct Context" .-> A4
    end
    A4 --> Output[Investment Decision Memo]
    
    style A1 fill:#f9f,stroke:#333,stroke-width:2px
    style A2 fill:#f9f,stroke:#333,stroke-width:2px
    style A3 fill:#f9f,stroke:#333,stroke-width:2px
    style A4 fill:#bbf,stroke:#333,stroke-width:4px