Context engineering fits business DNA in agentic AI systems

<p><b>AI needs more than just data to succeed; it needs direction and insight gleaned from your company’s DNA. Context engineering, a paradigm shift from prompt engineering, can help.</b></p>

<p>Recently, after a 4-mile, 40-minute run, I checked my heart rate on my workout app, as usual. On a whim, I then used the watch ECG app—which I thought was in deep trouble. It displayed a warning, asked about other symptoms, and told me to monitor such situations and see a doctor at the earliest opportunity.</p> <p>Clearly, my apps had not been picking up cues from one another. With minimal context from the workout app, the ECG app would have known I had just finished a run. It could have reported inconclusive results. It could have advised me to try an ECG after resting. Heck, it could even have congratulated me on my pace.</p> <p>A similar scenario is playing out across enterprises as they build AI agents and agentic AI-based systems using large language models (LLMs); a lack of context awareness is causing businesses to miss opportunities.</p> <p>In one example, we worked with a 401(k)-record-keeping service provider that had deployed an AI agent to support loan and withdrawal requests. Despite the AI, the client’s call center was seeing <i>higher</i> volumes and <i>longer</i> calls - the opposite of projected results.</p> <p>We discovered that when customers engaged the AI agent, it lacked context about them. For example, if Jane asked, “How much can I withdraw from my account?” the agent responded, “You can withdraw your vested balance, and if you are younger than 59½, you may have to pay a 10% penalty.” It forced Jane to answer multiple generic questions before arriving at an answer tailored to her situation. Customers grew frustrated, abandoned the AI agent and picked up the phone instead, destroying the business case for the agent.</p> <p>A better design might have initially responded: “Welcome, Jane. Your plan lets you withdraw up to $X for the following approved life events. Because you are 62 years old, you won’t be subject to a withdrawal penalty. I can get some information to initiate your withdrawal application.”</p> <p>Indeed, a sophisticated context-aware solution could continue: “However, I see you have an outstanding balance on a 401(k) loan for your first home, and we would like to understand your current situation better before processing a withdrawal. If you withdraw $X today, it may reduce your annual retirement income by $Z, starting from your preferred retirement age of 65.”</p> <p>Naturally, we helped our client improve its AI agent. As the example illustrates, without appropriate context, these agents may provide inaccurate results and will certainly waste time and effort; will deliver zero or suboptimal ROI; and could even cause promising AI initiatives to be abandoned.</p> <h4>Context engineering: A foundation for Agentic AI system engineering</h4> <p>Prompt engineering—tweaking the way we ask LLMs for answers—was a good start. But as LLMs become the brain behind complex, multi-agent workflows, we need a more robust approach. Enter context engineering: the practice of designing &quot; information conduits” that deliver the right data, instructions and guardrails to LLMs at the right time.</p> <p>Unlike prompt engineering, which focuses on crafting the perfect question, context engineering is about building a system that feeds the AI everything it needs: user prompts, system instructions (like roles and personas), previous interaction history, user preferences, company policies, knowledge bases and transactional data. All this must fit within the LLM’s context window and be delivered in a way that ensures the AI responds appropriately. This approach addresses key LLM limitations (including lack of memory, reliance on public data and limited context window size) by ensuring continuity of information and orchestration across interactions.</p> <h4>Context engineering: A strategic moat for the business</h4> <p>No two companies—even in the same industry—share the same context. Each has its own goals, culture, processes, knowledge bases, and constraints. Context engineering lets organizations harness this uniqueness, embedding proprietary knowledge and operational DNA into their AI systems.</p> <p>Embodying the collective wisdom of an organization—what worked, what didn’t, and why—context engineering enables continuous learning and adaptation. It maps interactions across teams, customers and partners, supported by the tools and data that drive execution and decision-making at every level.</p> <p>This tailored approach helps AI systems navigate both day-to-day operations and unexpected challenges, ensuring compliance with rules and regulations while remaining flexible. Ultimately, context engineering transforms AI from a generic tool into a strategic asset that reflects and amplifies what makes your business different.</p> <h4>Connecting goals and execution</h4> <p>We know that many AI projects fail to deliver real business value. A recent MIT study found that 95% of gen AI projects had little to no measurable impact on profits. It was a finding that sparked plenty of debate (we had <a href="https://uat.cognizant.com/us/en/insights/insights-blog/mit-ai-study-takeaways" target="_blank">some thoughts of our own</a>), but the core issue is clear: There’s a gap between what AI tools can do and how organizations use them. Context engineering bridges this gap by connecting organizational goals to execution, by leveraging both static and dynamic context.</p> <ul> <li><b>Static context</b> includes relatively unchanging elements like roles, rules, workflows, and guardrails. For example, a payments fraud AI agent might be programmed to flag large, complex sets of transactions based on currency amount, country, account type, etc. It might then apply specific compliance rules and invoke applicable escalation procedures.<br> <br> </li> <li><b>Dynamic context</b> includes evolving factors that can change, often rapidly, such as customer requests or real-time operational data. For instance, if a customer calls an insurer to update their car’s parking address, a context-aware AI agent can recognize this as a potential sales lead, create a CRM entry, and instruct the appropriate salesperson to pursue the opportunity. In manufacturing, context engineering allows AI agents to learn from sensor data, weather updates and operator feedback, and to adjust production and shipping schedules to mitigate supply-chain impacts.</li> </ul> <p>By identifying and filling in missing context, these systems evolve beyond rule-following, learning from new data points and adapting as conditions change.</p> <h4>The way forward</h4> <p>Building agentic AI solutions with robust context engineering isn’t a one-and-done task. It requires ongoing governance, iteration, and a commitment to keeping context relevant. At Cognizant, we approach this as a lifecycle: mapping both static and dynamic context, deploying this to production and analyzing feedback to evolve the system over time.</p> <p>In human-to-human interactions, we often refer to the <a href="https://www.google.com/search?q=seven\+cs\+of\+communication\&amp;safe=active\&amp;sca\_esv=514c60beb9213ab0\&amp;rlz=1C1GCEW\_enUS1079\&amp;sxsrf=AE3TifMgNLIYzeV5BK4gPi\_CbKZy\_I79zA%3A1761164157505\&amp;ei=fTv5aJPFHve4qtsP3bCuiQU\&amp;oq=seven\+Cs\&amp;gs\_lp=Egxnd3Mtd2l6LXNlcnAiCHNldmVuIENzKgIIADIFEAAYgAQyBRAAGIAEMgUQABiABDIFEAAYgAQyCxAuGIAEGMcBGK8BMgcQABiABBgKMgcQLhiABBgKMgUQABiABDIHEAAYgAQYCjILEC4YgAQYxwEYrwFIxzJQzQZYgh1wAngBkAEEmAGwAqABxQ6qAQc1LjUuMi4xuAEByAEA-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\&amp;sclient=gws-wiz-serp" target="_blank">seven Cs of communication</a>. We realized that driving orchestration among AI agents to deliver a common goal requires a more granular paradigm, and so we have defined <a>17 Cs </a>to capture the context engineering journey, as an integral part of Agent Development Lifecycle.</p>

<p><br> While the promise of agentic AI is huge, many leaders are understandably cautious about the risks of failed projects or “<a>hallucinating</a>” AIs. A disciplined context engineering approach helps mitigate these risks, accelerating time to value and building trust in AI-driven outcomes. Context engineering makes agentic AI systems reliable, scalable and enterprise-ready—and brings rigor to managing the cost of such solutions.</p> <p>Data may fuel AI engines, but context engineering provides the map—ensuring your AI gets where it needs to go, safely and efficiently.</p> <p>&nbsp;</p>