The Problem

Why MPLP?

AI agents don't just need prompts. They need a lifecycle protocol to ensure consistency, observability, and governance.

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The Crisis

The Multi-Agent Coordination Crisis

As organizations move from single-agent chat to complex multi-agent systems, they hit the same structural failures. Without a protocol, every team builds their own ad-hoc orchestration layer.

Context Drift

Agents lose context across long-running tasks. Without a shared semantic state, instructions degrade over time, leading to hallucinations and task failure.

Black Box Execution

Most agent runs are opaque. When something goes wrong, it's impossible to trace the decision chain or audit the specific prompt that caused the error.

Vendor Lock-in

Framework APIs change constantly. Building directly on a specific vendor's SDK locks your architecture to their roadmap and pricing.

Security Gaps

Agents often have unrestricted access to tools. Without a governance layer, there are no checks on what actions an agent can take or what data it can access.

Integration Hell

Connecting different models, vector databases, and tools requires custom glue code for every combination, creating a brittle and unmaintainable codebase.

No Standard Handover

Agents struggle to hand off tasks to one another. There is no standard format for passing context, state, and intent between different agent roles.

The Solution

A Protocol-First Approach

MPLP (Multi-Agent Lifecycle Protocol) solves these problems by defining a vendor-neutral standard for how agents plan, coordinate, execute, and govern their work.

Standardized Lifecycle

MPLP defines a clear lifecycle for every agent task: Intent → Plan → Execute → Verify. This ensures that every action is deliberate, traceable, and governed by policy.

  • Shared semantic context across all agents
  • Explicit planning and confirmation steps
  • Structured execution traces for auditability

Modular Governance

Instead of a monolithic framework, MPLP offers a set of composable modules for specific governance needs. You can adopt the whole protocol or just the parts you need.

  • Context Module: Manages shared state
  • Trace Module: Records execution history
  • Role Module: Defines agent permissions

Governance Core

The Agent OS Protocol

A protocol-level framing: MPLP governs agent frameworks and runtimes by enforcing lifecycle contracts—Plan, Confirm, Trace, and audit-grade semantics.

Read AgentOS Protocol →

Comparison

MPLP vs. The Rest

See how MPLP compares to ad-hoc scripts and proprietary frameworks.

Feature
Ad-hoc Scripts
Agent Frameworks
MPLP Protocol
Standardization
None
Vendor-specific
Open Standard
Observability
Manual logging
Built-in (Opaque)
Structural Trace
Interoperability
Low
Ecosystem-locked
Universal
Governance
None
Basic permissions
Policy-as-Code
Long-running
Fragile
Stateful (Memory)
Durable Context
Conformance Tests
None
Unit tests only
Golden Flows (Normative)
Evidence Chain
None
Logs only
Plan → Confirm → Trace

Governance

If you are deploying agents in production, governance cannot be optional.

See how MPLP treats frameworks, runtimes, and transports as governed objects—and how the Evidence Chain makes auditability structural.

AgentOS Protocol →Evidence Chain →Governed Stack →Verify via Compliance →