Welcome to TraceMem

TraceMem is a platform that provides governed execution boundaries for AI agent decisions. It ensures that every agent action is automatically tracked, policy-enforced, and auditable—creating immutable records of what happened, why it was allowed, and who approved it.

Core Principle

Agents can request actions, but they never authorize them. TraceMem enforces governance, records evidence, and creates audit trails. This separation of decision-making from authorization is what makes agent decisions trustworthy in enterprise environments.

What TraceMem Provides

TraceMem solves the fundamental challenge of deploying AI agents in production: how do you trust agents to make decisions that affect real business systems?

Instead of hoping agents make the right choices, TraceMem creates a governed execution boundary where:

• Every decision is tracked - Immutable decision traces record what happened and why
• Policies are enforced - Deterministic rules determine what's allowed before actions execute
• Approvals are required - Human judgment is integrated when policies require exceptions
• Data access is governed - Agents only access data through purpose-bound interfaces
• Evidence is preserved - Decision-time snapshots ensure "why" remains answerable forever

Main Components

Decision Envelopes

Every agent operation starts with a Decision Envelope—an execution boundary that tracks all operations within a decision context. Envelopes declare the intent (e.g., "customer.order.create"), automation mode (propose, approve, autonomous), and lifecycle state. All data access, policy evaluations, and writes happen inside an envelope.

Decision Traces

A Decision Trace is the permanent, immutable record of how a specific decision was made and executed. It answers "Why was this allowed?" by recording what data was consulted, which policies were evaluated, who approved exceptions, and what changed as a result. Traces are append-only, evidence-first, and replay-friendly.

Data Products

Data Products are the only way agents are allowed to read or write data. They're governed, purpose-bound interfaces that define what data is exposed, for what purposes, under which restrictions, and with which policies. Each data product supports exactly one operation (read, insert, update, or delete) and is versioned and immutable once published.

Policies

Policies are deterministic decision rules that determine whether actions are allowed. They evaluate at execution time and return allow, deny, or requires_exception. Policies are versioned, immutable once published, and recorded as evidence in decision traces. They define constraints, not workflows—answering "under what conditions is this allowed?" not "how should this proceed?"

Agents

Agents are AI systems that make decisions through TraceMem's governed execution boundary. They interact with TraceMem via Agent MCP (Model Context Protocol), creating decision envelopes, requesting data access, evaluating policies, requesting approvals, and executing actions within governed boundaries.

Approvals & Approval Routes

When policies return requires_exception, decisions escalate to Approval Routes that define how human judgment is integrated. Routes specify which integration to use (Slack, email, webhook), where to send requests, and expiration times. Approvals are recorded as decision events and become part of the immutable trace.

Connectors

Connectors link TraceMem to your data sources—databases, APIs, or other systems. Data Products reference connectors as sources, allowing agents to access data without direct database connections.

Key Aspects

Governance by Design

TraceMem doesn't add governance as an afterthought—it's built into every operation. Policies are evaluated before actions execute, not after. Data access requires explicit purposes. Decisions are tracked from creation to completion.

Immutable Audit Trails

Every decision creates a trace that cannot be modified. Decision-time snapshots capture policy versions, schema hashes, and data product versions, ensuring historical decisions remain explainable even as systems evolve.

Purpose-Bound Data Access

Agents must declare why they're accessing data (e.g., "order_processing", "support_triage"). This purpose is recorded in the trace, enabling compliance with GDPR, CCPA, and other privacy regulations.

Versioned, Immutable Artifacts

Policies and Data Products are versioned and immutable once published. Historical traces reference specific versions, so "why this was allowed" remains answerable even if rules change later.

Agent MCP Integration

Agents connect to TraceMem via Agent MCP (Model Context Protocol), a JSON-RPC 2.0 server that provides standardized tools for all operations. This protocol-agnostic approach works with any agent runtime or framework.

How It Works

A typical agent decision flows like this:

1. Agent creates a Decision Envelope - Declares intent and automation mode
2. Agent reads data through a Data Product - Specifies purpose and query
3. Agent evaluates policies - Checks if proposed action is allowed
4. If exception required - Agent requests approval via Approval Route
5. If allowed - Agent writes data through a Data Product
6. Agent closes the decision - Commits or aborts, creating the final trace

Throughout this flow, TraceMem automatically:

• Records every operation as a decision event
• Evaluates attached policies
• Captures decision-time snapshots
• Creates the immutable audit trail

Getting Started

Ready to build governed, auditable agent decisions? Start with the Quickstart Guide to create your first decision trace in minutes, or explore the Core Concepts to understand how TraceMem's components work together.

For agents integrating via MCP, see For AI Agents for complete integration context, or check the Agent MCP Reference for detailed API documentation.