Unwrapped

Teardown · cognition

COGNITION

COGNITION

CategoryAutonomous AgentsValuation · $2.0B · 2024Site ↗
  • Founders Fund
  • 8VC
  • Khosla Ventures
  • Thrive Capital

Frontier LLM APIs + browser + shell + persistent workspace.

01

Public data / API layer

Internal replication score

Easy
0.73

Feasibility of a useful internal substitute built with Claude (or similar), the same data access, and light agent logic — not rebuilding the whole product.

IRS = 0.30·D + 0.25·L + 0.20·O + 0.15·R + 0.10·Sthis record · 73%
  • D

    Data accessibility

    weight 0.300.95
    • 1.0mostly customer-owned / public / standard third-party sources
    • 0.5mixed accessibility
    • 0.0hard-to-access or proprietary source layer
  • L

    LLM substitutability

    weight 0.250.70
    • 1.0mostly retrieve / prompt / cite / summarize / classify / compare
    • 0.5mixed standard + custom behavior
    • 0.0strongly custom model behavior (fine-tunes on proprietary data, etc.)
  • O

    Output simplicity

    weight 0.200.75
    • 1.0straightforward internal work product (memo, list, reply, SQL query)
    • 0.5moderately specialized
    • 0.0highly specialized (e.g. FDA-graded clinical text)
  • R

    Review / risk tolerance

    weight 0.150.60
    • 1.0internal use with human review is acceptable
    • 0.5moderate risk
    • 0.0very low tolerance for error (e.g. external legal filings)
  • S

    Surface complexity

    weight 0.10inverse — higher means less surface dependence0.30
    • 1.0a simple internal shell is enough
    • 0.5polished workflow matters somewhat
    • 0.0product surface / rollout / trust posture is central to value
LabelsEasy ≥ 0.67Medium ≥ 0.34Hard < 0.34

Missing factor rows use heuristics from wrapper scores. Editorial heuristic, not investment advice.

Build it yourself

Recreate the workflow inside your org.

Internal build

Build it yourself

Same codebase access + frontier LLM API + agent loop in Docker — lower trust ceiling, manual review gates.

Internal use only. Replacing them in-market is a different bar than replaying the useful workflow inside your org.

01 · Connectors & flow

GitHub REST & GraphQL APIs
GitHub REST & GraphQL APIs
Public Web (via browser automation)
Public Web (via browser automation)
OpenAI API
OpenAI API
Anthropic Claude API
Anthropic Claude API
Customer Codebases
Customer Codebases
Customer Internal Tools & APIs
Customer Internal Tools & APIs

Internal build map

Data in

Connectors
Connectors

Agent layer

Planner
Tools + retrieval
Reasoning model

Logic

LLM API
plan
browse
code
test
retry
not custom weights

Outputs

Internal search
Answer
Citations

02 · Claude / agent prompt

Paste as the system or developer message in Claude (or your agent runtime). Scroll to read; Copy grabs the full text.

Claude / agent prompt

// Autonomous engineering agent for internal use You are an engineering agent working inside [YOUR_COMPANY]. You complete software engineering tasks autonomously using ONLY: - The company's GitHub repositories (read/write access) - A persistent shell session in an isolated container - A web browser for documentation and research - Standard development tools (git, npm, pytest, etc.) ## What you must do 1. Plan before executing: Break down the task, identify affected files, outline the approach. 2. Work incrementally: Make small changes, test frequently, commit often with clear messages. 3. Validate rigorously: Run all relevant tests, linters, type checks before claiming completion. 4. Document clearly: Write commit messages and PR descriptions that explain what changed and why. 5. Surface blockers: If you encounter ambiguous requirements, missing credentials, or test failures you can't resolve, report the issue clearly and wait for human input. 6. Scope conservatively: Do not refactor unrelated code, change APIs without approval, or deploy to production. ## What you are not You are not a replacement for code review. All PRs require human approval before merge. This is an internal tool for prototyping and task automation. ## Refusal Refuse if the task requires: - Production deployment without explicit human approval - Access to systems or credentials not already configured - Changes to security-critical code without senior review - Modifying code you cannot test ## Safety All work happens in isolated environments. No direct production access. Human engineers review all PRs before merge.

03 · Result

Fix the bug in user authentication where tokens aren't refreshing correctly
GitHub repo + test suite

Identified root cause in auth middleware, patched token refresh logic, added regression test, all tests passing.