Context Engineering in AI: Designing the Right Inputs for Smarter, Safer Large-Language Models

 

What Is Context Engineering?

In classic software, developers write deterministic code; in today’s AI systems, we compose contexts. Context engineering is the systematic craft of designing, organizing and manipulating every token fed into a large-language model (LLM) at inference time—instructions, examples, retrieved documents, API results, user profiles, safety policies, even intermediate chain-of-thought. Well-engineered context turns a general model into a domain expert; poor context produces hallucinations, leakage or policy violations. 

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Core Techniques

Technique	Goal	Typical Tools / Patterns
Prompt Design & Templates	Give the model clear role, task, format and constraints	System + user role prompts; XML / JSON schemas; function-calling specs
Retrieval-Augmented Generation (RAG)	Supply fresh, external knowledge just-in-time	Vector search, hybrid BM25+embedding, GraphRAG
Context Compression	Fit more signal into limited tokens	Summarisation, saliency ranking, LLM-powered “short-former” rewriters
Chunking & Windowing	Preserve locality in extra-long inputs	Hierarchical windows, sliding attention, FlashMask / Ring Attention
Scratchpads & CoT Scaffolds	Expose model reasoning for better accuracy and debuggability	Self-consistency, tree-of-thought, DST (Directed Self-Testing)
Memory & Profiles	Personalise without retraining	Vector memories, episodic caches, preference embeddings
Tool / API Context	Let models call and interpret external systems	Model Context Protocol (MCP), JSON-schema function calls, structured tool output
Policy & Guardrails	Enforce safety and brand style	Content filters, regex validators, policy adapters, YAML instruction blocks

Why It Matters

1. Accuracy & Trust – Fact-filled, well-structured context slashes hallucination rates and citation errors.

2. Privacy & Governance – Explicit control over what leaves the organisation or reaches the model helps meet GDPR, HIPAA and the EU AI Act.

3. Cost Efficiency – Compressing or caching context can cut token bills by 50-80 %.

4. Scalability – Multi-step agent systems live or die by fast, machine-readable context routing; good design tames complexity.

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High-Impact Use Cases

Sector	How Context Engineering Delivers Value
Customer Support	RAG surfaces the exact policy paragraph and recent ticket history, enabling a single prompt to draft compliant replies.
Coding Agents	Function-calling + repository retrieval feed IDE paths, diffs and test logs, letting models patch bugs autonomously.
Healthcare Q&A	Context filters strip PHI before retrieval; clinically-approved guidelines injected to guide safe advice.
Legal Analysis	Long-context models read entire case bundles; chunk ranking highlights precedent sections for argument drafting.
Manufacturing IoT	Streaming sensor data is summarised every minute and appended to a rolling window for predictive-maintenance agents.

Designing a Context Pipeline: Four Practical Steps

1. Map the Task Surface
   • What knowledge is static vs. dynamic?
   • Which external tools or databases are authoritative?

2. Define Context Layers
   • Base prompt: role, format, policy
   • Ephemeral layer: user query, tool results
   • Memory layer: user or session history
   • Safety layer: filters, refusal templates

3. Choose Retrieval & Compression Strategies
   • Exact text (BM25) for short policies; dense vectors for semantic match
   • Summaries or selective quoting for large PDFs

4. Instrument & Iterate
   • Log token mixes, latency, cost
   • A/B test different ordering, chunking, or reasoning scaffolds
   • Use self-reflection or eval suites (e.g., TruthfulQA-Context) to measure gains

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Emerging Tools & Standards

• MCP (Model Context Protocol) – open JSON schema for passing tool output and trace metadata to any LLM, adopted by Claude Code, Gemini CLI and IBM MCP Gateway.

• Context-Aware Runtimes – vLLM, Flash-Infer and Infinity Lite stream 128 K-1 M tokens with optimized KV caches.

• Context Observability Dashboards – Startups like ContextHub show token-level diff, attribution and cost per layer.

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The Road Ahead

As context windows expand to a million tokens and multi-agent systems proliferate, context engineering will sit alongside model training and fine-tuning as a first-class AI discipline. Teams that master it will ship assistants that feel domain-expert-smart, honest and cost-efficient—while everyone else will chase unpredictable black boxes.

Whether you’re building a retrieval chatbot, a self-healing codebase or an autonomous research agent, remember: the model is only as good as the context you feed it.

ARAG puts a multi-agent brain inside your RAG stack — and Walmart’s numbers look eye-popping

 Retrieval-augmented generation (RAG) has become the go-to recipe for giving large language models real-world context, but most deployments still treat retrieval as a dumb, one-shot lookup. Researchers at Walmart Global Tech think that leaves serious money on the table — especially in e-commerce, where user intent shifts by the minute. Their new framework, ARAG (Agentic Retrieval-Augmented Generation), adds a four-agent reasoning layer on top of vanilla RAG and reports double-digit gains across every metric that matters.

Four specialists, one conversation

1. User-Understanding Agent distills long-term history and the current session into a natural-language profile.

2. NLI Agent performs sentence-level entailment to see whether each candidate item actually supports that intent.

3. Context-Summary Agent compresses only the NLI-approved evidence into a focused prompt.

4. Item-Ranker Agent fuses all signals and produces the final ranked list.

Each agent writes to — and reads from — a shared blackboard-style memory, so later agents can reason over earlier rationales rather than raw text alone.

How much better? Try 42 %

On three Amazon Review subsets (Clothing, Electronics, Home), ARAG beats both a recency heuristic and a strong cosine-similarity RAG baseline:

Dataset	NDCG@5 ↑	Hit@5 ↑
Clothing	+42.1 %	+35.5 %
Electronics	+37.9 %	+30.9 %
Home & Kitchen	+25.6 %	+22.7 %

An ablation test shows that yanking either the NLI or context-summary modules knocks as much as 14 points off NDCG, underlining how critical cross-agent reasoning is to the win.

Why it matters

• Personalization that actually reasons. By turning retrieval and ranking into cooperative LLM agents, ARAG captures the nuance of why an item fits, not just whether embeddings are close.

• No model surgery required. The team wraps any existing RAG stack; there’s no need to fine-tune the base LLM, making the upgrade cloud-budget friendly.

• Explainability for free. Each agent logs its own JSON-structured evidence, giving product managers a breadcrumb trail for every recommendation.

The bigger picture

Agentic pipelines have taken off in code generation and web browsing; ARAG shows the same trick pays dividends in recommender systems, a multi-billion-dollar battleground where percent-level lifts translate into real revenue. Expect retailers and streaming platforms to test-drive multi-agent RAG as they chase post-cookie personalization.

Paper link: arXiv 2506.21931 (PDF)

Google’s MASS Revolutionizes Multi-Agent AI by Automating Prompt and Topology Optimization

 Designing multi-agent AI systems—where several AI "agents" collaborate—has traditionally depended on manual tuning of prompt instructions and agent communication structures (topologies). Google AI, in partnership with Cambridge researchers, is aiming to change that with their new Multi-Agent System Search (MASS) framework. MASS brings automation to the design process, ensuring consistent performance gains across complex domains.

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🧠 What MASS Actually Does

MASS performs a three-stage automated optimization that iteratively refines:

1. Block-Level Prompt Tuning
   Fine-tunes individual agent prompts via local search—sharpening their roles (think “questioner”, “solver”).

2. Topology Optimization
   Identifies the best agent interaction structure. It prunes and evaluates possible communication workflows to find the most impactful design.

3. Workflow-Level Prompt Refinement
   Final tuning of prompts once the best network topology is set.

By alternating prompt and topology adjustments, MASS achieves optimization that surpasses previous methods which tackled only one dimension 

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🏅 Why It Matters

• Benchmarked Success: MASS-designed agent systems outperform AFlow and ADAS on challenging benchmarks like MATH, LiveCodeBench, and multi-hop question-answering 

• Reduced Manual Overhead: Designers no longer need to trial-and-error their way through thousands of prompt-topology combinations.

• Extended to Real-World Tasks: Whether for reasoning, coding, or decision-making, this framework is broadly applicable across domains.

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💬 Community Reactions

Reddit’s r/machinelearningnews highlighted MASS’s leap beyond isolated prompt or topology tuning:

“Multi-Agent System Search (MASS) … reduces manual effort while achieving state‑of‑the‑art performance on tasks like reasoning, multi‑hop QA, and code generation.” linkedin.com

 

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📘 Technical Deep Dive

Originating from a February 2025 paper by Zhou et al., MASS represents a methodological advance in agentic AI

• Agents are modular: designed for distinct roles through prompts.

• Topology defines agent communication patterns: linear chain, tree, ring, etc.

• MASS explores both prompt and topology spaces, sequentially optimizing them across three stages.

• Final systems demonstrate robustness not just in benchmarks but as a repeatable design methodology.

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🚀 Wider Implications

• Democratizing Agent Design: Non-experts in prompt engineering can deploy effective agent systems from pre-designed searches.

• Adaptability: Potential for expanding MASS to dynamic, real-world settings like real-time planning and adaptive workflows.

• Innovation Accelerator: Encourages research into auto-tuned multi-agent frameworks for fields like robotics, data pipelines, and interactive assistants.

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🧭 Looking Ahead

As Google moves deeper into its “agentic era”—with initiatives like Project Mariner and Gemini's Agent Mode—MASS offers a scalable blueprint for future AS/AI applications. Expect to see frameworks that not only generate prompts but also self-optimize their agent networks for performance and efficiency.

AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges

 A recent study by researchers Ranjan Sapkota, Konstantinos I. Roumeliotis, and Manoj Karkee delves into the nuanced differences between AI Agents and Agentic AI, providing a structured taxonomy, application mapping, and an analysis of the challenges inherent to each paradigm. 

Defining AI Agents and Agentic AI

• AI Agents: These are modular systems primarily driven by Large Language Models (LLMs) and Large Image Models (LIMs), designed for narrow, task-specific automation. They often rely on prompt engineering and tool integration to perform specific functions.

• Agentic AI: Representing a paradigmatic shift, Agentic AI systems are characterized by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. They move beyond isolated tasks to coordinated systems capable of complex decision-making processes.

Architectural Evolution

The transition from AI Agents to Agentic AI involves significant architectural enhancements:

• AI Agents: Utilize core reasoning components like LLMs, augmented with tools to enhance functionality.

• Agentic AI: Incorporate advanced architectural components that allow for higher levels of autonomy and coordination among multiple agents, enabling more sophisticated and context-aware operations.

Applications

• AI Agents: Commonly applied in areas such as customer support, scheduling, and data summarization, where tasks are well-defined and require specific responses.

• Agentic AI: Find applications in more complex domains like research automation, robotic coordination, and medical decision support, where tasks are dynamic and require adaptive, collaborative problem-solving.

Challenges and Proposed Solutions

Both paradigms face unique challenges:

• AI Agents: Issues like hallucination and brittleness, where the system may produce inaccurate or nonsensical outputs.

• Agentic AI: Challenges include emergent behavior and coordination failures among agents.

To address these, the study suggests solutions such as ReAct loops, Retrieval-Augmented Generation (RAG), orchestration layers, and causal modeling to enhance system robustness and explainability.

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References

1. Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025). AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges. arXiv preprint arXiv:2505.10468.