How to improve AI agent performance with context engineering

AI agents have evolved from simple task executors to sophisticated systems capable of complex reasoning and decision-making. Yet despite advances in model capabilities and architectural improvements, many organizations struggle to achieve consistent, reliable performance from their AI deployments.

The challenge lies not in making AI agents more autonomous, but in providing them with the right context to make informed decisions. AI agent performance collapses in realistic scenarios, with conversation correctness falling to just 14.1% for GPT-4o and 10.4% for Claude 3 Sonnet in multi-turn dialogue tasks despite 90%+ accuracy on single-turn benchmarks. Context engineering has emerged as the critical discipline for maximizing AI agent effectiveness, shifting focus from expanding agent independence to optimizing the information environment in which they operate.

Context engineering represents a fundamental shift in how we approach AI agent development. Rather than focusing solely on model improvements or expanding autonomous capabilities, context engineering strategically designs and manages the information environment that AI agents use to understand tasks, make decisions, and execute actions. This discipline recognizes that even the most advanced AI models are only as effective as the context they operate within. Enterprise agents optimized for accuracy alone cost 4.4-10.8x more than cost-aware alternatives with comparable performance, while domain-specialized approaches achieved 72.8% reliability compared to general models dropping 19.4% from single-run performance.

At its core, context engineering involves three critical components: data curation, information architecture, and dynamic context management. Data curation ensures that AI agents receive high-quality, relevant information free from noise or contradictions. Information architecture structures this data in ways that AI agents can efficiently parse and understand — using techniques like semantic tagging, hierarchical organization, and relationship mapping. Dynamic context management adapts the available information based on the task at hand, preventing information overload while ensuring agents have access to everything they need. At 32,000 tokens of context length, 11 state-of-the-art language models drop below 50% of their performance levels achieved on shorter contexts, demonstrating systematic 'context rot' as information volume increases.

Organizations that master context engineering see dramatic improvements in AI agent performance: more accurate responses, better alignment with business objectives, and reduced errors from hallucination or misinterpretation. The key is understanding that context serves as the foundation upon which all AI agent capabilities are built.

What is context engineering for AI?

Context engineering represents a fundamental shift in how we approach AI agent development. Rather than focusing solely on model improvements or expanding autonomous capabilities, context engineering strategically designs and manages the information environment that AI agents use to understand tasks, make decisions, and execute actions. This discipline recognizes that even the most advanced AI models are only as effective as the context they operate within.

At its core, context engineering involves three critical components: data curation, information architecture, and dynamic context management. Data curation ensures that AI agents receive high-quality, relevant information free from noise or contradictions. Information architecture structures this data in ways that AI agents can efficiently parse and understand — using techniques like semantic tagging, hierarchical organization, and relationship mapping. Dynamic context management adapts the available information based on the task at hand, preventing information overload while ensuring agents have access to everything they need.

The evolution from prompt engineering to context engineering reflects a deeper understanding of how AI agents process information. While prompt engineering focused on crafting the perfect instruction, context engineering acknowledges that AI agents need more than just clear directions — they require a comprehensive understanding of their operational environment. This includes organizational knowledge, domain-specific terminology, historical patterns, and the relationships between different pieces of information. When Arvind Jain, former Google search engineer, developed enterprise search solutions, he discovered that authority algorithms needed fundamental redesign for enterprise contexts, where document relationships and user permissions created entirely different relevance patterns than consumer search.

Successful context engineering transforms AI agents from reactive tools into proactive partners. By providing rich contextual understanding, agents can anticipate needs, identify patterns humans might miss, and make connections across disparate information sources. This approach has proven particularly effective in enterprise environments where context includes not just explicit data but also implicit knowledge about team dynamics, project histories, and organizational culture. Model Context Protocol (MCP) exemplifies this evolution, providing structured ways for AI agents to access tools, memory, and contextual information without overwhelming their processing capabilities.

How to improve AI agent performance with context engineering

Enhancing AI agent performance begins with prioritizing precise and structured data. This ensures that AI systems operate with clarity and accuracy, reducing the chance of errors. Establishing strong data management practices allows for consistent and reliable information flow, which is crucial for informed decision-making.

Implementing systems that efficiently organize information is vital for contextual understanding. These systems should categorize and tag information logically, enabling quick access and seamless integration into AI workflows. By structuring data effectively, AI agents can draw meaningful insights and navigate complex tasks with greater ease.

Promoting seamless communication between AI agents through standardized protocols enhances collaborative problem-solving. Protocols for agent interactions enable the sharing of insights and knowledge, mirroring effective teamwork in enterprise environments. This collaboration leads to more integrated solutions, allowing agents to leverage shared intelligence for improved outcomes.

A dynamic framework guiding AI actions ensures adaptability and context-aware decision-making. By mapping relationships and understanding patterns, AI can engage in sophisticated reasoning and adjust strategies as situations evolve. This adaptability allows AI agents to respond intelligently to new challenges and optimize their performance continuously.

Step 1: enhance data quality

Effective input structuring empowers AI agents to navigate tasks with precision. Employing well-defined directives enhances clarity, allowing agents to process tasks with greater accuracy. For example, LinkedIn reported a 78% accuracy improvement in customer service AI by incorporating enterprise knowledge graphs, while Morgan Stanley saved 20% of knowledge workers' time weekly using federated enterprise knowledge graphs. By using targeted instructions, you ensure agents align closely with intended objectives.

Correcting data inaccuracies before they are integrated into AI systems is crucial. This involves meticulous verification and cleansing techniques to eliminate errors, thus ensuring AI agents base their actions on reliable data. Effective data management practices strengthen the foundation upon which AI systems build their insights.

Adopting consistent data formats enhances interoperability and ease of integration. This uniformity across datasets simplifies the process of data interpretation and application, enabling AI systems to leverage information efficiently. Standardized formats support seamless interaction and reduce complexity in handling diverse data inputs.

Utilizing knowledge graphs provides a structured approach to understanding complex information networks. By establishing connections and relationships within data, these graphs offer AI agents a comprehensive view of the context. This capability enhances multi-step reasoning and uncovering patterns, boosting the agent's ability to process and interpret intricate data landscapes.

Step 2: design effective input structuring

Effective input structuring empowers AI agents to navigate tasks with precision. Employing well-defined directives enhances clarity, allowing agents to process tasks with greater accuracy. By using targeted instructions, you ensure agents align closely with intended objectives.

Logical organization of data is vital for seamless AI interaction. Utilizing structured formats such as XML tagging or Markdown headers enhances data accessibility and coherence. This method enables agents to quickly identify relevant information and understand context, facilitating efficient decision-making.

Maintaining a balance between detailed guidance and adaptability is essential for resilient AI performance. Providing structured frameworks allows agents to follow a consistent strategy while also adapting to changing conditions. This dynamic approach supports the agent's ability to tackle complex scenarios with agility and intelligence.

Step 3: manage context dynamically

Effectively managing context in real-time is crucial for AI agents to excel. Implementing just-in-time data retrieval allows agents to access the exact information needed at the precise moment, enhancing their ability to deliver accurate and timely responses. This strategy ensures that agents maintain a sharp focus on relevant data, thus optimizing their decision-making capabilities.

Utilizing streamlined identifiers facilitates quick and seamless data navigation. These markers act as efficient guides, directing AI systems to pertinent information without unnecessary complexity. By employing these efficient pathways, AI agents can efficiently traverse intricate data environments, remaining agile and responsive in dynamic situations.

Incorporating sophisticated memory and feedback systems empowers AI agents to continually refine their operations. These mechanisms enable agents to learn from each interaction, broadening their expertise and ensuring they remain adaptive to new challenges. This continuous evolution prevents rigidity in outputs, allowing AI agents to handle a wide array of tasks with proficiency and insight.

Step 4: optimize tool usage

Integrating advanced workflows and adaptive memory systems transforms AI operations. Advanced memory systems reduced token usage from 27,000 to 2,000 tokens at 300 conversations—a 13x improvement—while maintaining 70-75% accuracy and eliminating sequential processing bottlenecks. These elements collaborate to ensure that AI agents remain agile, continuously refining their processes and delivering significant value in diverse enterprise settings. This strategic focus on context empowers AI to drive productivity and foster innovation across industries.

Integrating advanced workflows and adaptive memory systems transforms AI operations. These elements collaborate to ensure that AI agents remain agile, continuously refining their processes and delivering significant value in diverse enterprise settings. Organizations implementing sophisticated AI workflow automation achieved 422% ROI over three years with service costs falling 88% and agent productivity climbing 50%, while banking implementations delivered 250-400% ROI. This strategic focus on context empowers AI to drive productivity and foster innovation across industries.

Tools should prioritize delivering concise and relevant information, minimizing unnecessary processing. This approach enhances the agent's ability to act quickly and accurately, supporting seamless integration into complex workflows. Efficient tool performance is key to maintaining overall system responsiveness.

Continuous evaluation and refinement of toolsets ensure they meet the evolving demands of AI applications. By integrating specialized tools that complement each other, agents can improve decision-making processes and adapt to changing technological landscapes. This synergy between tools and AI capabilities maximizes operational effectiveness.

Final thoughts

Enhancing AI agent performance hinges on leveraging context to improve decision-making processes. By equipping AI agents with comprehensive situational awareness, they can seamlessly integrate into complex environments, ensuring actions are both effective and aligned with strategic objectives. This approach not only bolsters their functionality but also fosters innovation and problem-solving.

Clearly defining AI agent roles is essential for tailored deployment and maximized impact. A precise understanding of their capabilities allows organizations to strategically implement AI systems, enabling them to operate effectively within their intended scope while harnessing their full potential for creative solutions.

Integrating advanced workflows and adaptive memory systems transforms AI operations. These elements collaborate to ensure that AI agents remain agile, continuously refining their processes and delivering significant value in diverse enterprise settings. This strategic focus on context empowers AI to drive productivity and foster innovation across industries.

The future of AI agent performance lies not in greater autonomy, but in smarter context engineering that transforms how your teams work. When AI agents understand your organization's unique context — from data relationships to team dynamics — they become true productivity multipliers rather than just task executors. Ready to see how enterprise AI can work seamlessly within your environment? Request a demo to explore how Glean and AI can transform your workplace and discover what's possible when we combine advanced AI with deep contextual understanding tailored to your needs.