Context Engineering for Multi-Agent Systems

Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design, strengthen, and apply across real-world scenarios. Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol (MCP). As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot seamlessly across domains, from legal compliance to strategic marketing, proving its domain independence. By the end of this book, you’ll be equipped with the skills needed to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence.

• Glass Box Architecture: Provides 100% observability into agent reasoning through interactive trace dashboards and detailed execution logs.
• Universal Context Engine: A domain-agnostic core that runs cross-domain use cases (e.g., Legal and Marketing) without changing a single line of code.
• Dual High-Fidelity RAG: Implements research agents(dual: instructions and facts) with automated input sanitization and source-verifiable citations to ensure accuracy and defense.
• Telemetry‑driven context layers: Continuous ingestion and structuring of environmental signals that form the dynamic operational context for multi‑agent reasoning.
• Protocol-Driven: Orchestrates specialized agents using the Model Context Protocol (MCP) for seamless, modular multi-agent workflows.
• Token & Cost Analytics: Integrated tracking of input/output tokens to monitor cost-efficiency and model verbosity at every step.
• Substrate Independence: The same architecture runs on OpenAI + Pinecone, on LangChain/LangGraph, on a local DeepSeek-R1, on NVIDIA Nemotron with a real-time DAG planner, and — in claude_projects/ — with no code at all, as natural-language protocol files. The contracts are the engine; every implementation is a carrier.

Computer use can be added to the Context Engine as a new agentic capability — extending the architecture from reasoning about an environment to directly perceiving and acting within it. With GPT-6 Astra, an agent can receive visual state, reason over the environment, select computer actions, and interact with a controlled execution layer. The local harness becomes the bridge between the model and the environment, providing screenshots, action execution, permissions, human oversight, and safety controls.

This creates a new capability layer for the Context Engine:

Context → Reasoning → Perception → Action → Environment → Observation → Context

The capability is deliberately modular. Computer use can sit alongside the Context Engine's existing RAG, memory, MCP, telemetry, governance, and multi-agent components, providing an additional perception-and-action interface for digital environments. The same architectural principle can eventually extend from software environments toward increasingly complex physical environments — making governance, security, human oversight, and sovereign deployment essential parts of the architecture.

👉 How to organize your GPT-6 Astra Computer-Use Workshop-documentation and code in Python for your team

• Develop memory models to retain short-term and cross-session context
• Craft semantic blueprints and drive multi-agent orchestration with MCP
• Implement high-fidelity RAG pipelines with verifiable citations
• Apply safeguards against prompt injection and data poisoning
• Enforce moderation and policy-driven control in AI workflows
• Repurpose the Context Engine across legal, marketing, and beyond
• Deploy a scalable, observable Context Engine in production

✅ The Levels of Efficient Context · ✅ Dual RAG · ✅ Agent Orchestration · ✅ Compliance & Risk

Stop tinkering with prompts. Start engineering context. Most AI implementations fail at scale because they rely on black-box prompting — sending a request into the void and hoping for a coherent reply. Following the success of our January session, Cohort 2 of this hands-on workshop is now open. We move beyond simple instructions to build a Context Engine: a transparent, glass-box architecture where agents don't just guess — they execute a precise, structured plan.

The workshop frames the new software stack as a delegation gradient across four runtimes — from the human running a context engine in their head, through embedded copilots, configured platforms, and engineered systems. Mastery of the underlying tiers is what makes any of them deployable. We close with the question that sits underneath every enterprise AI decision in 2026: which tier does this problem belong in, and what does compliance actually require?

Save thousands of lines of code by building universal, domain-agnostic Multi-Agent Systems (MAS) using the ultimate new programming language: natural language, engineered as context.

🧭 The Tiers of Context Engines — Tier 3 → Tier 2 → Tier 1.5 → Tier 1

⚖️ Compliance & Risk Management — GDPR · HIPAA · SOC 2 · ISO · FedRAMP

This recorded session walks through the entire stack behind the sentence: “In 21st‑century Agentic AI, Natural‑Language‑Programmed LLMs are the agents, and the domain‑agnostic dual‑RAG MAS is the environment they operate in.” The deep dive unpacks each term step‑by‑step:

• 21st‑century Agentic AI — why agents are natural‑language‑programmed programs
• LLMs as agents — how reasoning, memory, and protocols turn models into actors
• Domain‑agnostic Context Engine — the universal core that runs any use case
• Dual‑RAG MAS — the two‑channel research architecture (instructions + facts)
• Environment design — how telemetry, context layers, and MCP orchestrate agents
• Full drill‑down to code — notebooks, pipelines, and execution traces
• Full climb back up — how the code re‑forms the architecture end‑to‑end
  📺Watch the full deep dive on LinkedIn
  If you are an architect or lead looking for:
  ✅ ROI & Domain Agnosticism logic
  ✅ Glass-Box Observability traces
  ✅ Sovereign RAG blueprints
  Join the engineering discussion here: Link to GitHub Discussion

✅ Prerequisites

• Python: Version 3.10+
• Environment Options:
  • Google Colab or
  • Local Python environment with:
    • openai
    • pinecone # (formerly pinecone-client — the package was renamed)
    • tiktoken
    • tenacity
    • nest_asyncio # required by the NIM edition's notebook (Colab kernel + asyncio)
    • fastapi # only if you run the Gradio/deployment notebooks in Chapter 10

🚀 Quick Start

Get up and running using cloud-based virtual machines using the Google Colab links provided for each notebook.
No local installation is required.

1. Get Your API Keys

• OpenAI: Sign up and generate a key at platform.openai.com.
• Pinecone: Sign up and generate a free API key at pinecone.io.
• NVIDIA NIM (only for the NIM edition): free key at build.nvidia.com.

2. Run the Notebooks

Click the badges below to launch the notebooks directly in a pre-configured Google Colab VM. You will be asked to add your API keys to the Colab Secrets Manager upon launch.

✅ Project Structure

Create a GitHub or local workspace containing at least:

• helpers.py
• agents.py
• registry.py
• engine.py
• Notebook files for each chapter

✅ Required API Keys

• OpenAI – model access and moderation
• Pinecone – vector database storage and retrieval
• NVIDIA NIM (NIM edition only) – planning and agent inference
• (Optional) Google Cloud or AWS – for deployment sections in Chapter 10

✅ System Requirements

Note: From Chapter 5 onward, modular components depend on earlier notebooks. Ensure your environment is configured correctly, as setup steps may not be repeated in later chapters.

✅ Additional Notes

• Local execution may incur token and API costs with large contexts.
• The Summarizer Agent (Chapter 6) helps reduce token usage.
• Familiarity with RAG workflows and MCP-based agent orchestration is recommended.
• Refer to Appendix: Context Engine Reference Guide for quick lookup of component structures and explanations.

Denis Rothman is an AI systems architect and author whose work bridges foundational AI research with today’s generative and agentic architectures. A graduate of Sorbonne University and Paris‑Diderot University, he designed one of the earliest patented word2matrix numerical encoding systems which was a precursor to modern embedding techniques. He designed one of the first industrial conversational agents, deployed as an automated language teacher for Moët & Chandon and other global companies. Throughout his career, Denis has built large‑scale AI systems across industries, from IBM resource optimizers to worldwide Advanced Planning and Scheduling (APS) solutions, always focusing on transparent, explainable, and production‑ready architectures. Building on decades of applied AI engineering, he has become a leading voice in the agentic era of AI, authoring influential books on transformers, RAG pipelines, business‑ready generative AI, and now Context Engineering for Multi‑Agent Systems. His work emphasizes model‑agnostic engineering, semantic design, and the construction of resilient, domain‑independent AI systems that go far beyond prompting. Denis continues to publish hands‑on frameworks, open‑source architectures, and practical guides that help engineers, researchers, and organizations build the next generation of verifiable, context‑driven AI systems.

• Bring AI to the data: RAG-Driven Generative AI, Second Edition "The era of extracting sensitive enterprise data to external AI platforms is under fire. This second edition defines a revolutionary architectural shift: bringing the AI to the data. This repository shows you how to build Sovereign AI systems that keep your most critical assets secure and governed within Oracle's converged engine, eliminating the fragmentation, latency, and security risks inherent in traditional data extraction. The repository implements Oracle AI Database 26ai, powered by the Oracle Database 23ai engine, and opens the door to other AI‑to‑data databases such as SQL Server or any database you are integrating."
  Architecture & Code: Universal Context Engine, Converged Edition · RAG Overview (data-in-place)
• Bring humans to AI for supervision and quality: Building Business-Ready Generative AI Systems
  Architecture & Code: Guardrails and Security · Human-Facing Customer Service · Short- and Long-Term Session Memory · AI as a Live Meeting Participant
• Explore where it all began and is evolving: Transformers for Natural Language Processing and Computer Vision, Third Edition
  Architecture & Code: Multi-Head Attention from Scratch · DeepSeek-R1's Training Innovations

We welcome contributions! High interaction through Issues, PRs, and Comments helps the Context Engine grow and improves the trending visibility for the community.

How to get started:

1. Check Issues: Look for the good first issue label for approachable tasks.
2. Discussions: Join our Discussions tab to propose new features or "Context Chaining" techniques.
3. Pull Requests: Submit improvements to the core engine.py or new specialized agents in agents.py.

Cool code is great.

But without a compelling Return on Investment (ROI), it will never survive in production.

Agentic AI should therefore be evaluated on more than model capability or technical sophistication:

Capability → Context → Governance → Execution → Measurable Business Value

The objective is not to build the most impressive demo.

The objective is to build systems that are useful, governable, measurable, and economically sustainable.

What happens when an AI architecture can act?

This is where my thinking about the architecture is evolving.

I think we are crossing an important architectural boundary in agentic AI.

OpenAI describes GPT-6 Astra as state-of-the-art in computer use, with the ability to carry out multi-step workflows across browsers, software and professional environments.

I have now added a computer-use capability to my Context Engineering for Multi-Agent Systems repository.

But the interesting part isn't the model.

It is the architecture around the model.

The repository now demonstrates a progression:

Context → Reasoning → Perception → Action → Environment → Observation → Context

And that capability sits alongside:

• Dual high-fidelity RAG
• Multi-agent orchestration
• MCP
• Telemetry
• Governance gates
• Glass-box traces
• Prompt-injection defenses
• Token/cost analytics
• Sovereign/local execution
• Human oversight

The same architectural contracts are demonstrated across different substrates: OpenAI, LangChain/LangGraph, DeepSeek-R1, NVIDIA Nemotron, Claude Projects — and now computer use with GPT-6 Astra.

That leads to a distinction I think will become increasingly important:

The model is not the agentic system.

The model provides intelligence.

The protocol provides the contract.

The harness provides controlled execution.

The environment provides the world in which actions have consequences.

And governance determines what the system is allowed to do.

This is also why I am deliberately treating computer use as a capability of the Context Engine, rather than creating another "model edition."

It extends the architecture rather than replacing it.

The repository is educational — not an operational deployment — and is being developed alongside my book, training sessions and corporate workshops.

The goal is to make the architecture visible, testable and teachable, including the uncomfortable parts: sovereignty, security, governance and human control.

I also think this changes the scope of what we mean by context engineering.

When an AI system only generates text, context largely determines what the system knows, remembers and reasons about.

When the system can perceive and act, context becomes part of a much larger control loop.

It determines what the agent can understand, what environment it can observe, what actions it can consider, what tools it can access, what permissions it has, and what evidence is captured after it acts.

That is a very different engineering problem.

And it is one reason I believe the architecture around increasingly capable models matters as much as the models themselves.

Because once an AI system can perceive and act, context engineering is no longer only about what an agent knows.

It is also about what it can see, what it can do, and what we allow it to do.

The Potential Architectural Shift

This raises another critical question for me. Where should deterministic automation end and agentic automation begin?

If we combine a sovereign, governable Context Engine with organizational anchors, domain knowledge, governance controls, and a full domain-agnostic context architecture — and then add the perception-and-action capability of GPT-6 Astra — could we simplify some of the increasingly cumbersome automation stacks used in large enterprises?

I don't mean simply replacing RPA.

I mean reconsidering where we need RPA, workflow engines, Microsoft automation components, custom integrations, or any automated ordering tool in the first place.

Traditional enterprise automation often looks something like:

Process → Script → Application → Result

The agentic architecture I am exploring looks more like:

Context → Reasoning → Governance → Action → Environment → Observation → Context

With computer use, the agent can potentially operate existing enterprise applications through their interfaces rather than requiring every interaction to be represented as a separate deterministic integration.

That creates an interesting architectural possibility:

                    ┌─────────────────────┐
                    │ Enterprise Context  │
                    │                     │
                    │ Policies            │
                    │ SOPs                │
                    │ Domain Knowledge    │
                    │ RAG / Memory        │
                    │ Organizational      │
                    │ Anchors             │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Reasoning / Planning│
                    └──────────┬──────────┘
                               │
                        Governance Gate
                               │
                               ▼
                    ┌─────────────────────┐
                    │ GPT-6 Astra         │
                    │ Perception + Action │
                    └──────────┬──────────┘
                               │
                               ▼
             ┌─────────────────────────────────┐
             │ Existing Enterprise Environment │
             │                                 │
             │ ERP │ CRM │ Browser │ Microsoft │
             │ Legacy Apps │ Web Apps │ etc.   │
             └────────────────┬────────────────┘
                              │
                              ▼
                    Observation / Evidence
                              │
                              ▼
                    Context + Audit Trail

That changes the proposition from:

How do we automate this application?

to:

How do we govern an intelligent system that can accomplish this business outcome across the applications we already use?

For me, that is a much more interesting question.

And it brings the discussion directly back to ROI.

The objective should not be to replace a technology simply because a newer technology exists.

The objective should be to determine whether the new architecture can produce measurable improvements.