By Shashi Jagatap

The rise of agentic AI has ushered in a paradigm shift in how we design, deploy, and scale intelligent systems. At the center of this evolution are two deeply intertwined ideas: Context Engineering and Multi-Agent orchestration. Both offer powerful tools—but they solve different problems, and applying one without understanding the role of the other can quickly lead to fragile, overengineered, or underperforming systems.

Context engineering refers to the delicate art and science of curating and optimizing the context window within which large language models (LLMs) operate, while multi-agent strategy involves designing systems where multiple autonomous agents interact to solve a problem or complete a task. In this article, we will explore the relationship between context engineering and multi-agent strategy, discussing when it is beneficial to orchestrate multiple agents and when a solo agent approach is more suitable.

What Is Context Engineering—And Why Is It Surpassing Prompt Engineering?

To set the stage, let's first define what context engineering entails. It all started from the X thread from Spotify CEO’s tweet about Context Engineering followed by a quote from Andrej Karpathy. It highlights that simply providing a prompt to an LLM is not enough, but rather “context engineering” is required. This involves carefully curating the information provided to the LLM, including task descriptions, examples, and relevant data, to ensure optimal performance. Context engineering is a critical aspect of system design for technical engineers building autonomous Agentic AI systems. It involves optimizing the performance of agents and enabling them to tackle complex tasks by providing the right information, tools, and format to complete tasks successfully. This process includes task descriptions and explanations, few-shot examples, retrieval-augmented generation (RAG), multimodal data, tools, state, and history, as well as context compaction.

The objective is to deliver precisely the right context to address complex, custom tasks effectively. Where prompt engineering is about telling the model what to do, context engineering is about giving it the conditions to reason effectively. It’s what allows agents to operate in dynamic environments, manage ambiguity, and adapt over time—whether they act alone or as part of a team. For Agent builders, there are two aspects of context engineering: Technical Context Engineering for AI engineers and developers, focusing on optimizing LLM performance, and User Context Engineering for everyday users, teaching them how to effectively interact with LLMs by providing the right context and information.

In short, Context Engineering is a delicate balance between providing too little or too much information, and that doing it well requires a combination of science (technical expertise) and art (intuition and understanding of LLM psychology).

What Is Agentic AI—and What Are Multi-Agent Systems?

There is no standard definition so far, either for "Agent" or "Agentic AI", but in general, Agentic AI refers to a type of artificial intelligence that enables agents to interact with their environment and make decisions based on their goals and preferences. Multi-agent systems, on the other hand, involve multiple autonomous agents interacting to solve a problem or complete a task. These agents may specialize in different skills and can work in parallel or in sequence or can be orchestrated as per need. At its best, a multi-agent system behaves like a well-orchestrated team, dividing work, sharing context, checking each other's outputs, and reaching better outcomes than a monolithic agent could on its own. 

The Challenges of Multi-Agent Systems

Building Multi-Agent systems involve the integration of multiple Language Models, which can vary in size and scope, including large, medium, and small models, or combinations thereof, each with its own unique specialty. However, coordinating multi-agent systems can be challenging. Poor design can lead to agents miscommunicating, looping indefinitely, or making conflicting decisions. It is crucial to strike a balance and make informed decisions based on context and specific use cases, determining when to leverage Multi-Agent systems and when to opt for alternative approaches. The development of multi-agent systems poses several significant challenges, which can be distilled into five core areas of concern:

1) Fragmented Decision-Making and Context Loss: A primary obstacle in multi-agent systems is the lack of a shared understanding among agents, resulting in incomplete or partial access to the broader decision-making context. This can lead to incoherent behavior, redundant actions, and conflicting decisions, particularly when agents fail to retain memory across steps or share state effectively.

2) Communication and Coordination Limitations: Agents often lack robust mechanisms for negotiation, conflict resolution, and shared planning, which can be attributed to the limitations of current communication models. The absence of structured protocols hinders agents' ability to synchronize their efforts, rendering parallelism fragile and prone to inefficiencies.

3) Scalability and Performance Bottlenecks: As the number of agents increases, the complexity of managing interactions, communication overhead, and resource contention also grows. Inadequate orchestration can result in delays, deadlocks, or breakdowns in collaboration, ultimately undermining potential performance gains from concurrency.

4) Inconsistency, Hallucination, and Reliability Issues: Agents operating with disparate knowledge states or outdated information can produce contradictory outputs, leading to inconsistencies and unreliable behavior. The lack of consistent grounding in shared memory or facts can also render the system prone to hallucinations and degraded trust in outcomes.

5) Debugging, Security, and Emergent Behavior Risks: Multi-agent systems are inherently challenging to monitor and debug due to the subtle inter-agent dynamics that can give rise to problems. Each additional agent increases the system's complexity and attack surface, while emergent behaviors – although sometimes beneficial – can be unpredictable and difficult to control, ultimately making the system less safe and more challenging to manage.

The Benefits of Multi-Agent Systems

While building multi-agent systems presents several challenges, many experts view these complexities as opportunities to leverage the benefits of inference, computation, and vendor-agnostic design. By harnessing the capabilities of both large and small language models, multi-agent systems can be optimized to achieve enhanced performance and efficiency. The following advantages can be realized through the implementation of multi-agent systems:

1) Parallel, Breadth-First Exploration: Multi-agent systems enable the simultaneous exploration of multiple facets of a complex question by deploying multiple subagents. This parallelism accelerates discovery, increases the coverage of potential leads, and significantly reduces completion time compared to linear, single-agent workflows.

2) Separation of Concerns through Specialization: By delegating tasks to specialized subagents, each with its own prompts, tools, and context window, the system effectively isolates subtasks. This modular design prevents path dependency, improves clarity, and enhances the overall coherence of task execution.

3) Unmatched Scaling of Reasoning Capacity: Distributing token budgets across multiple agents with independent context windows dramatically boosts reasoning capacity. Recent data from an Anthropic blog post has shown that this approach can yield high performance gain over single-agent models on research tasks, largely driven by increased token usage and parallel tool utilization.

4) Dynamic, Adaptive Planning: A central orchestrator (lead agent) observes subagent outputs, updates strategy mid-stream, and can spawn new subagents or redirect focus as new information emerges. This dynamic approach mimics human research workflows and supports flexible pivots during complex problem-solving.

5) Robust Synthesis and Citation Integration: Following parallel exploration, a final synthesis stage – often performed by a citation or summarizer agent – aggregates findings, compiles outputs, and ensures proper attribution. This promotes reliability, transparency, and traceability in system outputs, ultimately enhancing the overall credibility and trustworthiness of the results.

As some see it as a challenge while others see an opportunity, this sparked debate between Anthropic and Cognition. 

Anthropic vs Cognition: Two Paths 

The debate surrounding multi-agent systems was recently ignited by two landmark posts from Anthropic and Cognition. Anthropic shared how a multi-agent system supercharged their AI research workflows, while Cognition countered with a cautionary tale, advising against building multi-agent systems unless absolutely necessary. The question of who is right does not have a definitive answer, as it is contingent upon various factors. The outcome depends on the specific problem being addressed, the tools and technologies utilized, the design of the architectural framework, and, most importantly, the manner in which agents coordinate, communicate, and retain information.

The key difference between Anthropic's and Cognition's approaches lies in the context engineering aspect. Anthropic engineered a system where agents shared full traces, remembered decisions, and worked on tasks suited for parallelism. In contrast, Cognition described systems that lacked shared state, coordination discipline, and clear communication channels. This highlights the importance of context engineering in multi-agent systems, where agents must have access to shared memory, prior conversations, decisions made by other agents, and evolving goals.

The challenges associated with multi-agent systems can be attributed to the lack of shared context, coordination discipline, and clear communication channels. Anthropic vs. Cognition's debate underscores the importance of context engineering in determining the success or failure of multi-agent systems. When agents share full context, remember decisions, and work on tasks suited for parallelism, multi-agent systems can be highly effective. However, when agents lack shared state, coordination discipline, and clear communication channels, the results can be disjointed and chaotic. In light of these challenges, it is essential to consider when to orchestrate multiple agents and when to stay solo.

Multi-Agent Systems: Best Practices

Drawing from expertise at Anthropic, Cognition, and real-world deployments, the following essential principles for building successful multi-agent systems can be applied in the real world design:

1) Judicious Use of Multi-Agent Setups: Deploy multi-agent architectures only when the task necessitates it, such as in exploratory research, complex tool-rich environments, or workflows that benefit from high parallelism.

2) Context Sharing: Ensure that all agents have access to a shared context, encompassing decisions, intermediate results, failures, and current objectives.

3) Customized Agent Engineering: Design each agent with tailored memory, feedback mechanisms, and behavior profiles to optimize performance.

4) Continuous Evaluation and Optimization: Employ a combination of automated metrics and human judgment to assess and refine system performance continually.

5) Intentional Orchestration: Carefully design workflows and delegation logic to avoid improvisation, ensuring that each component works in harmony towards a common goal.

6) Robustness and Failure Handling: Incorporate safety nets, retry mechanisms, and transparency into every agent's process to mitigate the risk of failure and ensure system resilience.

It is essential to recognize that multi-agent systems are not inherently magical solutions. However, when meticulously engineered and designed with these principles in mind, they can yield outcomes that surpass the capabilities of their individual components.

Why Context Engineering Is Essential—Even in Multi-Agent Systems

Context engineering plays a vital role in the success of both solo and multi-agent systems, serving as a crucial complement to prompt engineering. While prompt engineering focuses on individual instructions, context engineering ensures that agents possess the necessary memory, tools, and shared understanding to make informed decisions.

In solo agent systems, context engineering enables continuity by allowing the agent to retain memory of prior steps, user goals, and evolving tasks. However, in multi-agent systems, context engineering is even more critical. The absence of shared context, including previous decisions, current objectives, and other agents' outputs, can result in agents operating in isolation, leading to duplication, confusion, or failure.

As Large Language Model (LLM) systems continue to grow in complexity, context engineering has become the foundational element of reliable orchestration. It enables agents to collaborate effectively, adapt dynamically, and remain aligned with overall system objectives. In this paradigm, agents require access to a shared knowledge base, which encompasses prior conversations, decisions made by other agents, and dynamically evolving goals. The absence of such a shared context would result in agents operating in isolation, rendering effective coordination and collaboration virtually impossible.

In today's complex LLM environments, context engineering is no longer an optional consideration – it is a critical differentiator between fragmented agent systems and fully functional, cohesive systems. 

Conclusion: Orchestrate When Necessary, Contextualize Always

In conclusion, context engineering and multi-agent strategy are two critical concepts in the development of sophisticated AI systems. While multi-agent systems offer many benefits, including parallelism, specialization, and adaptability, they also demand rigor, memory, and orchestration. By understanding the importance of context engineering in both solo and multi-agent systems, developers can design more effective AI systems that tackle complex tasks with precision and creativity. Ultimately, the choice between orchestrating multiple agents or staying solo depends on the specific problem being solved and the design of the architecture. By carefully considering these factors and prioritizing context engineering, developers can unlock the full potential of AI systems and create more intelligent, adaptive, and effective solutions.

References:

• Anthropic Blog: https://www.anthropic.com/engineering/built-multi-agent-research-system

• Cognition Blog: https://cognition.ai/blog/dont-build-multi-agents

• Context Engineering X Post: https://x.com/karpathy/status/1937902205765607626

• Superagentic AI Post on Context Engineering and Multi Agent System: https://super-agentic.ai/resources/super-posts/multi-agent-or-not-that-is-the-question/

• Langchain Blog on Context Engineering: https://blog.langchain.com/context-engineering-for-agents/