Harness Engineering Design Patterns for Securing Long - Horizon Multi - Agent AI Systems

Category: eBooks

views: 16
date: 5 November 2026
posted by: BeMyLove

Harness Engineering Design Patterns for Securing Long - Horizon Multi - Agent AI Systems | 10.53 MB

Title: Harness Engineering Design Patterns for Securing Long - Horizon Multi - Agent AI Systems
Author: Ken Huang, Prof. Yale Li, Iftach Orr, Matthew Versaggi, Dr. Melan Xu
Category: Systems Analysis, Software Development, Artificial Intelligence
Language: English | 327 Pages | ISBN: 9781809204141

Description:

Build a secure control plane for agentic AI with design patterns for bounded autonomy, least privilege identity, trusted context, governed memory, and runtime recovery

Key Features

Design a security control plane for long running agents and delegated multi-agent work

Contain prompt injection and memory poisoning through enforceable trust boundaries

Use HarnessLab scenarios to connect design patterns with observable control decisions

Book Description
Long-running AI agents need controls that hold across tool calls, delegated tasks, and persistent memory. Harness Engineering presents design patterns for securing long-horizon multi-agent AI systems through a control plane that governs autonomy, identity, intent, context, and memory. Learn to scope agent permissions, contain prompt injection, detect goal drift, and place human oversight where consequential actions require it.
Through architectural diagrams and HarnessLab scenarios, explore decision tracing, evaluation gates, runtime intervention, recovery, fleet coordination, and data governance. Build the engineering judgment to make agent behavior observable, authority accountable, and improvement measurable, so you can move from promising prototypes to controlled production operations.

What you will learn

Set autonomy budgets and risk based human approval gates

Scope agent identities, credentials, and delegation chains

Bind tool authority to intent and detect goal drift

Contain prompt injection and govern persistent memory

Use decision traces and evals to govern agent improvement

Steer live runs and enforce fleet and data boundaries

Who this book is for
For AI engineers, security architects, platform teams, and technical leaders designing or governing long running agentic AI systems. Researchers and graduate students will also benefit from its pattern based treatment of agent security and control. Familiarity with LLM applications, tool calling, access control, and distributed systems is recommended. The book supports readers moving from agent prototypes to accountable enterprise operations.

Table of Contents

Harness as the Control Plane for Long-Horizon Multi-Agent Systems

Autonomy: Graduated Control and Human Oversight

Identity: Per-Agent Identity and Delegation Chains

Intent: Contracts, Drift Detection, and Authorization by Purpose

Context: Trust Tiers, Provenance, and Context-Poisoning Containment

Memory: State Firewall, Ledger, and Forgetting

Observability, Evals, and Hill-Climbing Evidence

Runtime Steering: Intervention, Policy Injection, and Safe Rollback

Scalability: Fleet Coordination Without Cascading Failure

Data Governance: Classification-Aware Tools, Egress, and Compliance

DOWNLOAD:

https://rapidgator.net/file/6bd92778c8ab9fede873a0703153c9b5/Harness_Engineering_Design_Patterns_for_Securing_Long-Horizon_Multi-Agent_AI_Systems.rar

https://clicknupload.click/yzgkk04685an/Harness_Engineering_Design_Patterns_for_Securing_Long-Horizon_Multi-Agent_AI_Systems.rar

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Harness Engineering Design Patterns for Securing Long - Horizon Multi - Agent AI Systems

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