Context Engineers - The New Profession of the AI Era
Prompt engineering has quietly transformed into context engineering.
Today's models are so smart that it's no longer about HOW to ask, but WHAT to put in the context.
Context engineering is the science and art of filling the context window with exactly the information needed for the next step.
Why Context Matters More Than Prompts
We used to spend a lot of time figuring out how to formulate requests to models. What words to use, how to structure prompts, where to place examples. That was prompt engineering.
But modern models understand virtually any formulation. GPT-4, Claude, Gemini - they don't stumble if you say "write code" instead of "please create a program."
The problem is different: the model might lack the necessary information for a quality response.
This is where context engineering comes in.
What is Context Engineering?
It's the art and science of filling the context window with exactly the information needed for the next step.
Science - because it's system and structure:
Task descriptions and technical documentation
Few-shot examples for "on-the-fly" model training
RAG (Retrieval-Augmented Generation) - smart knowledge base search
Multimodal data - text, images, audio, video
Tool and state management in agentic systems
Noise filtering - removing unnecessary information is as important as adding the right stuff
Information compression without losing meaning
Structured formats: JSON, XML, delimiters
Art - because you need intuition. Feeling the model, understanding its "psychology," predicting what will work and what will ruin the result.
Why is this hard?
Too little context → model can't cope, outputs generic phrases
Too much → costs rise, quality drops, model gets confused by details
Wrong context → results completely miss the target
What's Included in Modern Context Engineering?
Dynamic prompt management - they're no longer static templates, but adaptive chains that change depending on the situation.
Smart RAG - not just vector search through knowledge bases, but relevant, conscious supply of models with needed knowledge at the right moment.
Memory management - short-term (dialogue history, intermediate results) and long-term (knowledge graphs, indexes, entity cards).
Input/output optimization - proper data structures, JSON schemas, XML markup, delimiters. Models work better with structured data.
Multimodal integration - working not just with text, but with images, audio, sensor data. Context becomes richer and more precise.
Tool management - in agentic systems, you need to properly pass context between different AI tools, APIs, and external services.
Context compression - compressing large volumes of information without losing meaning. When you have thousands of pages of documentation but can only fit a dozen in context.
Cognizant is Hiring 1,000 Context Engineers
On August 29, 2025, Cognizant announced a strategic initiative: they'll hire 1,000 context engineers within a year.
Cognizant is partnering with Workfabric AI and their ContextFabric™ platform, which transforms companies' "organizational DNA" - processes, data, rules, workflows - into actionable context for AI agents.
What these engineers will do:
Capture enterprise knowledge - processes, rules, task execution patterns
Manage the full context lifecycle - from collection to updates
Build integration pipelines for retrieval, synthesis, storage, and context distribution
Create reusable "context packs" for scalable deployment
Design contextual blueprints for industry-specific agentic solutions
Create ecosystems for context sharing
Result: AI agents that work like real employees. Fast, efficient, secure.
As Cognizant CEO Ravi Kumar S said: "In the microprocessor era, the lever was code. In the cloud era, it was workload migration. In the LLM era, the lever is context."
It's interesting how Cognizant is sacrificing part of their traditional business model. Instead of selling developer man-hours, they're investing in an AI-first approach.
I'm seeing the same thing with other giants.
Globant: From Man-Hours to Tokens
Speaking of outsourcing company transformations - Globant is having tough times:
Shares dropped 53% in 2025: from ≈$238 to ≈$100
Q1 revenue - $611M (+7% YoY), but growth rates slowed
Annual forecast lowered: expecting around +2% instead of +9%
Against this backdrop, Globant introduced a radically new model: AI Pods with token-based pricing instead of man-hours.
What is an AI Pod?
Imagine a combination of several strong programmers + corporate know-how + a cluster of agentic AI tools on the Globant Enterprise AI platform.
One Pod is claimed to be the monthly equivalent of approximately five senior engineers, but instead of man-hours, Globant counts tokens.
How it works:
Globant Enterprise AI is middleware that provides LLM access, corporate document indexing, and built-in security guardrails. AI Pods integrate with SAP, Salesforce, and other key client systems.
Advantages of the new model:
Tokens instead of hours - payment strictly for resources consumed by models and people
Security and flexibility - data stays within the organization's perimeter, but LLMs can be switched without rebuilding the entire solution
But there are calibration complexities:
How to determine token bucket size for expected load?
What are the overage rates and auto-scaling rules?
Need real-time dashboards so teams can optimize prompts before hitting limits
High stakes: Globant has 31,000 employees. If the token model works - some people will move to platform teams, some to product, some to AI Pods themselves.
If it doesn't work - the stock decline could drag on.
Context Engineers = Architects of Corporate Knowledge
Context engineers aren't just a new profession. They're architects of corporate knowledge.
They build the framework in which AI can think, decide, and act safely using the company's accumulated experience.
Their mission: turn corporate experience, processes, and data into real AI work results.
What they specifically do:
Analyze company knowledge - what processes, rules, decisions have been accumulated over years of work
Structure information - translate chaotic data into AI-understandable formats
Create contextual maps - schemes of what information is needed for different types of tasks
Set up filters - remove noise, keep only relevant information
Test and optimize - check how AI works with different context sets
Update knowledge - maintain context relevance when business changes
Why AI Doesn't Scale Without Them
The pilot hell problem is familiar to every large company: AI projects work great in demos but don't launch in production.
The reason is often that:
Pilots used ideal, manually prepared data
In reality, data is dirty, unstructured, contradictory
AI doesn't know the context of decision-making in the company
There are no mechanisms to update knowledge when business processes change
The problems context engineers solve:
Risk Reduction - agents operate in accordance with corporate standards and regulatory requirements
Higher ROI - accurate, reliable agents increase adoption rates
Efficiency Gains - fewer errors and rework
Cost Optimization - streamlined architectures instead of workarounds
Accelerated Time-to-Value - reusable assets and context libraries
Differentiation - context as the embodiment of company strategy in the execution model
Bridge Between Knowledge and Intelligence
Context engineers are the bridge between corporate knowledge and machine intelligence that actually works.
Without them, large-scale AI deployment is impossible. You can train as many models as you want, but if they don't understand how decisions are made in your company, they'll produce beautiful but useless results.
Real-life example:
An AI assistant for a bank can excellently generate texts about credit products. But if it doesn't know internal risk assessment procedures, loan approval criteria, and regulatory requirements - it will give advice the bank can't execute.
A context engineer will configure the system so AI:
Knows current procedures and limits
Understands regulatory constraints
Considers decision history on similar cases
Updates knowledge when rules change
Result: AI becomes a truly useful tool, not just a pretty demo.
What to Study if You Want to Become a Context Engineer
Technical skills:
RAG systems and vector databases
Prompt engineering and chain-of-thought
Working with various LLM APIs
JSON, XML, structured data
Knowledge graphs and ontologies
MLOps and AI system monitoring
Business skills:
Business process analysis
Stakeholder interviewing
Knowledge base documentation
Change management
Risk assessment
Useful resources:
Prompting Guide - comprehensive guide to prompting techniques
The Rise of Context Engineering - overview article from LangChain
12-Factor Apps for AI Agents - principles for building reliable AI agents
The Future of the Profession
Context engineering isn't a temporary trend. It's a fundamental discipline of the AI era.
As models become smarter, context quality becomes the main differentiator.
Companies that learn to properly structure and feed their knowledge to AI systems will gain serious competitive advantages.
Those who rely on generic AI without business-specific customization will be left with pretty demos and no real value.
Context engineers are the architects of this future. They're building the foundation on which AI can truly work in enterprise.
And this is just the beginning.