What is Deprex

Deprex is a platform that realizes Palantir’s Forward Deployed Engineer (FDE) concept using AI. AI agents forward deployed into client organizations autonomously observe and learn human tasks, progressively displacing them.

What is a Forward Deployed Engineer

FDE is a concept pioneered by Palantir, referring to engineers who embed directly within client organizations to solve problems. Deprex’s AI FDE fulfills this role with AI agents.

How it differs from conventional AI tools:

• AI chatbot: Answers questions
• AI assistant: Executes tasks when instructed
• AI FDE: Autonomously observes organizational tasks, learns from them, and executes them on behalf of humans

The AI FDE autonomously repeats the following cycle:

1. Observe: Monitors Slack conversations and business workflows to detect task patterns
2. Learn: Records detected patterns as task flows and auto-generates them as skills
3. Displace: Automatically executes human tasks using the generated skills. Validates quality through Shadow Comparison before transitioning to production displacement
4. Improve: Automatically collects feedback from execution results and autonomously improves skills

• Notifies the responsible person according to the team’s operational rules

The value of an FDE lies not in AI’s answers themselves, but in being embedded within the organization’s workflow and operating continuously.

What Deprex Solves

Deprex is the foundation for safely providing FDEs to multiple tenants. Each tenant has independent configuration, skills, and memory.

A Tenant PM can work with Deprex from the following perspectives, even without being a developer:

• Decide which Slack channels to receive notifications in
• Design which tasks to make into scheduled tasks
• Record tenant-specific rules and prerequisite knowledge in memory
• Organize requirements for custom skills
• Determine whether changes are tenant scope or shared scope

Key Features

Slack Integration

Deprex receives @mentions on Slack and begins working based on the content. The FDE distinguishes the weight of requests, separating quick replies, short tasks, and complex work.

What matters for PMs is that Slack serves as both the “request channel” and the “result notification channel.” There’s no new admin panel to learn — you can make requests in your usual channels.

@fde Summarize only the price-related inquiries from this week
@fde Compile the unresolved tasks from last week for tomorrow's standup

Scheduled Tasks

Deprex can automatically execute recurring jobs defined in YAML. This is suited for repetitive tasks like daily reports, weekly summaries, and monthly checks.

app/orgs/acme/scheduled-tasks.yaml

tasks:
  - name: weekday_kpi_report
    schedule: "0 9 * * 1-5"
    command: "Check yesterday's key KPIs and post a summary to Slack"
    env_file: "config/.env"
    description: "Weekday morning KPI check"

Scheduled tasks reduce manual reminders and prevent reporting gaps.

Persistent Memory

The FDE references context from memory each time it works. Memory stores team structure, product prerequisites, key KPIs, regular operations, and more as Markdown.

For example, the following types of information are well-suited for memory:

• Key personnel and their roles
• KPIs reviewed weekly
• Expression rules used in reports
• Notes specific to customers or departments

On the other hand, short-term task lists or frequently changing numbers should be managed through external tools or reports, not memory.

Extensible Skills

A skill is a business capability that can be added to the FDE. You write the purpose, inputs, procedure, outputs, and constraints in a SKILL.md file.

Shared skills are generic capabilities available to all tenants. Tenant-specific skills define processing tailored to a specific company’s tasks, terminology, and report formats.

app/orgs/acme/skills/
  weekly-business-review/
    SKILL.md
    scripts/

PMs don’t need to write skill code. However, defining “when to use it,” “what the inputs are,” and “what correct output looks like” makes it easier for developers to implement.

Multi-Tenant Architecture

Deprex separates harnesses by tenant. This prevents one tenant’s memory or Slack configuration from mixing with another’s.

app/orgs/
  acme/
    skills/
    memory/
    config/
  beta-inc/
    skills/
    memory/
    config/

This isolation enables small, safe operational changes per tenant.

What PMs Should Decide First

When introducing Deprex, organizing the following items upfront makes the process smoother:

• Routine tasks to delegate to the FDE
• Slack channels for requests and notifications
• Business knowledge and existing documents to reference
• Report frequency, format, and reviewers
• Decision points requiring human review

Positioning Against Other AI Agents

Multiple systems claim to “act autonomously.” To clarify Deprex’s position, here is the design contrast against general-purpose OSS agent runtimes (such as the Hermes Agent family).

Aspect	General OSS agent runtime	Deprex
Target user	Individual developers / one-off SaaS automation	B2B customers requiring an embedded operator
Central metaphor	Personal learning agent	FDE deployed in the customer organization
Memory	Personal notebook (few KB)	Organizational wiki (per-tenant knowledge graph)
Cognitive loop	Single agent loop	Bidirectional network + multi-layer guardrails
Self-improvement	Per-PR review	Multi-axis daemons running continuously
Multi-tenant	Not central to the design	Central (tenant isolation as a first-class concept)
Completion check	Trial-and-error test pass	Structural verification with acceptance criteria + evidence type

General OSS chooses “broad and shallow”; Deprex chooses “narrow and deep.” Deprex commits deeply to challenges intrinsic to embedded operation (tenant isolation, long-term knowledge accumulation, judgment-quality assurance, autonomy throughput), achieving quality that general-purpose tools cannot reach.

The two are not competitors. They solve different problems. General-purpose OSS is sufficient for one-off Slack/Discord automation, but Deprex is the choice when you need an agent embedded in the operational flow that continuously observes, learns, and displaces work.

Important Notes

The FDE is powerful, but don’t design with the assumption of automating everything. It’s safest to start with easy-to-verify reports, summaries, and notifications.

Confidential information, credentials, and proprietary implementation details should not be written directly in public documents or memory. Handle them through appropriate configuration files and access controls. In particular, internal harness implementation details should be described abstractly in tenant-facing documentation.