What is context engineering?

Estimated reading time: 5 minutes

Key takeaways:

• Context engineering optimizes what AI agents know, not just what they’re told.
  
• The key is balance: too much context overwhelms, too little limits usefulness.
  
• When done well, context engineering improves trust, speed, and code quality. 

As developer teams integrate more AI tooling into their workflows, a new practice is emerging: context engineering.

In software development, context engineering is the practice of supplying relevant, optimized information to AI coding agents to enhance their awareness and improve results.

Whereas prompt engineering fine-tunes instructions within AI chat conversations, context engineering focuses on what a model has access to under the hood. This includes information relevant to the problem at hand, like code, documentation, error logs, incident reports, domain-specific knowledge, available tools, and more.

Supplying AI-based coding agents with more context helps prevent generic, boilerplate suggestions. “Without context, no amount of clever prompting will get you a reliable answer,” said Guy Gur-Ari, co-founder and chief scientist at Augment Code, an AI software development platform.

However, context is a double-edged sword. Give agents too much data, and you cause confusion, bloat context windows, and drain token use. Give them too little, and they’re not much help at all. Context engineering helps find that sweet spot.

How does context engineering work?

Context engineering goes beyond earlier approaches to refining agent behavior in software development, such as prompt engineering or retrieval-augmented generation (RAG). The latter primarily helps AI retrieve one-off documents when generating a response.

At a technical level, context engineering boils down to which information and tools you expose to the large language model (LLM) at the heart of an agent. This helps the LLM enrich its responses and programmatically decide its next course of action.

The easiest way to enact context engineering is by using system prompts. These are found in most AI tools and accept instructions that help define an agent’s role, goals, and constraints. System prompts can also include few-shot examples that demonstrate target input and output behaviors.

According to experts, establishing context for AI agents involves a mix of structured and unstructured data types. Core areas include:

• System behaviors: code and documentation.
• System architecture: database schemas and deployment configurations.
• Code events: commits, pull requests, and review threads.
• Error information: tickets, failure logs, build output, and feedback from linters or compilers.
• Rationale: chat histories and design documentation.
• Business rules: compliance policies and operating procedures.
• Team behaviors: common workflows and execution patterns.

“This data is used to inform reasoning, guide execution, align with goals, and enable adaptive learning,” said Babak Hodjat, chief AI officer at Cognizant, an IT consulting company that recently announced plans to deploy over 1,000 context engineers within the next year.

Beyond static data, context engineering can also supply hyperlinks to additional sources or expose tools that an agent can take action on at runtime. The latter can be enabled with Model Context Protocol (MCP), a standard for connecting AI agents to external data, platforms, and APIs.

An emerging practice is to configure MCP servers in an AI coding environment and express these tools within a configuration file that lists which servers an agent can access. For instance, Multiplayer’s MCP could be invoked in this way to enhance AI-driven bug fixes with more granular, session-level context.

Many other MCP servers can provide AI coding agents with data tailored to the goal at hand, such as reading local files via a filesystem MCP, pulling public repository context from GitHub, or fetching insights from collaboration platforms like Asana.

Context engineering use cases

One area where context engineering shines is in investigating production errors. Equipped with detailed service codes, recent commits, full error logs, and an incident ticket, an agent could deliver more relevant results than when given only an opaque error code.

“That information would help it to identify exactly what went wrong and offer a targeted code fix,” said Gur-Ari. With the right context, such as issue trackers, bug reports, and schemas all in one place, agents can also help debug regressions more effectively.

Another use case for context engineering is reducing information overload. “When there are too many tokens in context, LLMs struggle to focus,” said Mrinal Wadhwa, CTO at Autonomy, makers of a platform for developers to ship autonomous products. Instead of feeding a model large  amounts of data, teams can provide lightweight indexes, schemas, or summaries.

For example, a pharmaceutical company’s agent struggled when given several hundred regulatory submission documents at once. Instead, they pivoted to using sub-agents, each with a lightweight catalog of other documents, and applied just-in-time search to retrieve specific sections only when needed. “This worked extremely well because each sub-agent only passed a very small amount of information to its LLM, in each iteration,” said Wadhwa.

Context engineering can also streamline voice-based AI agents for recruitment interviews, Wadhwa said. By compressing long conversational histories into concise summaries, developers can improve accuracy and consistency.

How to get context engineering right

Adopting context engineering properly takes upfront effort to gather the correct data, continually update knowledge to keep it fresh, and make these patterns reusable across an engineering organization.

Experts recommend using a consistent format, metadata, and structure to simplify data retrieval by AI. “Simple markdown or structured JSON are good for formatting inputs,”  saidWadhwa, who advocates just-in-time retrieval and sub-agents, each with a lean context scope, when working with large data catalogs.

“The core best practice is to treat context like code: explicit, consistent, and testable,” said Gur-Ari. This equates to establishing engineering rigor and deliberate validation over the components and prompts you share with agents.

Benefits of context engineering

According to the 2025 Stack Overflow report, only 3% of developers highly trust the accuracy of AI tools.  A study from CodeRabbit, which analyzed nearly 500 open-source pull requests, found that AI-assisted correctness issues, including business logic errors, misconfigurations, and unsafe control flows, rise 75% when using AI assistants.

The hope is that context engineering can help train AI coding tools on the history and style of a codebase and organizational knowledge, overcoming some of these gaps and increasing trustworthiness in AI agent outputs.

“You’ll get better answers, but more importantly, you’ll get them much faster because the agent doesn’t need to go hunting for the right answers,” said Gur-Ari. He ranks speed, efficiency, and code quality as high benefits of context engineering.

Beyond software teams, context engineering can also improve business user experiences with enterprise agents, making their responses more reliable and better aligned with user intent.

AI coding tools are proliferating, accelerating power users in the process. Due to its many benefits, context engineering is a smart strategy that developer leaders should consider adding to their team’s arsenal of engineering optimizations to support ROI in the AI age.

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