The artificial intelligence community has coalesced around the idea that context engineering forms the foundation for successful applications. Success is defined as reliably solving an organization’s specific workflow and automation challenges over time and at scale, within functional and governance constraints.
Experts from CTG Federation, a Cohesive Technology group company, say: If prompt engineering asks a question of a large language model, context engineering establishes the boundaries for answering that question: the world in which the prompt exists.
This idea resonated with workshop participants from Los Alamos National Laboratory, Sandia National Laboratories, the National Nuclear Security Administration, system integrators, and commercial customers. This should appeal to any government agency that is using agent AI to augment its human workforce to handle repetitive or administrative tasks.
In other words, context engineering is the science of determining what occupies what is called the window that an agent process invokes. This means you only need the information, data, workflows, tools, and resources needed to realize your intent every time, without constantly entering manual prompts.
The term large-scale language model itself implies an endless amount of available data to process billions of prompts. In fact, for locally effective AI applications, thinking of the execution window, or context, as a budget rather than an empty bucket to fill can help keep compute costs under control while minimizing latency, token costs, and erroneous outputs. Think of the context window as a restriction on the tokens and data that your model processes to ensure reliability while minimizing cost.
Just as Lean development techniques reduced software bloat in the days of hand-coding, limiting data, tokens, and tools helps keep AI applications lean, fast, and accurate. Moreover, too much “garbage” in the context window can lead to all sorts of negative effects, including addiction, distraction, confusion, and conflicts between facts. All of these reduce accuracy and efficiency.
agent foundation
This means that the context should be designed so that the agent retrieves only the exact data it needs, rather than preloading all the data. maybe need. Therefore, a carefully limited set of windows is preferable to one in which too much happens. As a result, the individual windows are simpler, which has the added benefit of making them easier to debug. As CTG Federal states, visible context is debuggable context.
Context engineering requires us to think differently about how we plan. for example:
- The previous question was about how to devise the best prompts. Now organizations need to ask themselves, what is the correct layout for this call?
- The question of which model to use requires architectural considerations. What do you store, retrieve, compress, insert, and separate? Did you know that CTG Federal and Black Arc can deliver 3-year-old model performance by Frontier model standards?
- The mystery of how something went wrong becomes asking what was inside the window that caused it to fail.
Models don’t do much on their own. If the model is like a CPU, the context is like a program.
Building reliable execution windows for AI applications using context engineering requires the right building blocks.
Just like the human employees they support, agents need skills. CTG Federal Engineers defines skills as procedural memory that is packaged and stored almost like a folder. Packages are retrieved and inserted as needed.
Agents also have an identity. Agent IDs have slightly more complex requirements than standard application requirements. However, like general identity, agent identity also represents its privileges and limitations. CTG Federal says, “Skills are how agents do their jobs. Identity is how agents decide what to do.”
Ultimately, the goal of an agency or agent AI is to execute workflows and offload mechanical tasks from humans.
Goal: Workflow
CTG Describe your workflow with federal language analogies. As the company’s engineers put it, “Workflows are skills organized into reusable steps. If skills are verbs, workflows are sentences.” Steps involve controlled versions and testing.
From another perspective, a workflow is the sum of many separate loops executed in succession. An important characteristic for large-scale business workflows is reproducibility. This is in contrast to the hit-and-miss results of workflows initiated by prompts.
Note that workflow execution includes checkpoints that allow humans to verify that the agent is working properly. Workflows are also designed to generate an artifact called workflow memory. The resulting trail allows employees to audit what their agents have done and suggests guidance to improve it.
These elements form what CTG Federal calls the agent loop within which the model operates. In fact, an agent consists of a model that performs inference functions and a tool that performs them. To complete the loop, the agent has the ability to observe and modify what it does in a self-correcting manner.
A well-designed agent loop ensures that your model only interacts with specific tools. The loop monitoring and revision feature prevents the context window from growing endlessly and consuming more memory. Fundamentally, the loop design maintains the reliability of the agent.
Surrounding the model and loop core is a harness.
An agent “worker” consists of six functions: tools, memory, validation, planning, workspace, and scheduling. Harnesses add loops, guardrails, and observability orchestration for compliance that really matters. This acts as a control for the model before anything is executed. This enables the deployment of agent workers in critical federal environments.
Add it all up and you’ll see that your agency has the ability to use agent AI to build true colleagues. It starts with context engineering, surrounded by carefully designed models, agents, and procedures that create reproducibility, reliability, and observability so that they can be adjusted.
Where do you start? CTG Federal and its partner Dell Technologies suggest asking people five questions as a way to chart the path forward.
- What to do about repetition?
- What types of reports and other output do you need to generate on a daily basis?
- How do we handle all this?
- How do you transfer artifacts between disparate systems?
- How much time do we spend looking for and getting things?
The answers reveal what people consider the pain points of their daily work. These are the places where agent automation is most useful.
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