Agent Skills: Context Engineering Framework for AI Agents

Key Takeaway

Agent Skills for Context Engineering is a modular framework that establishes Context Engineering as a distinct discipline for managing AI agent attention budgets. Created by Muratcan Koylan, it solves the 'lost-in-the-middle' phenomenon where agents lose coherence in long conversations by teaching them to curate context actively rather than treating it as passive storage.

What is Agent Skills for Context Engineering?

Agent Skills for Context Engineering is a comprehensive framework that defines and implements 'Context Engineering' as a critical discipline separate from standard prompt engineering. The project Agent Skills for Context Engineering solves the problem of attention degradation that we all face when building production AI agents - where the agent gets progressively 'dumber' as conversations lengthen and context windows fill up.

While prompt engineering focuses on crafting instructions for what agents should do, Context Engineering focuses on HOW agents manage the limited attention budget across system prompts, tool definitions, conversation history, and retrieved documents. Think of it like the difference between telling someone what tasks to complete versus teaching them how to prioritize and manage their attention across competing demands.

The Problem We All Know

We've all experienced this frustration: we're having a productive conversation with an AI agent, building context over dozens of messages. Then we reference something from earlier in the conversation, and the agent acts like it never happened. We find ourselves repeating information, re-establishing context, or worse - watching the agent contradict itself based on information it gave us twenty messages ago.

This isn't a model intelligence problem - it's an attention management problem. The technical term is 'lost-in-the-middle,' where important context gets buried under the noise of accumulated tokens. Every system prompt, every tool definition, every message in history, every retrieved document - they all compete for the same limited attention budget within the context window.

Existing solutions typically focus on making models smarter or expanding context windows. But nobody has been systematically teaching agents HOW to curate what deserves their attention in the first place.

How Agent Skills for Context Engineering Works

This framework introduces modular 'Agent Skills' - static, platform-agnostic instruction sets that agents can load dynamically to handle complex context management tasks. The approach is built on solid cognitive architecture principles, specifically implementing BDI (Belief-Desire-Intention) models.

Here's the key innovation: instead of treating the context window like a dump truck where we throw everything in and hope the model figures it out, Context Engineering teaches agents to act like museum curators. A curator doesn't keep every artifact - they actively choose what deserves display based on relevance, significance, and available space. That's exactly what these Agent Skills do for context management.

BDI Cognitive Architecture

The framework implements BDI (Belief-Desire-Intention) models, meaning agents transform external context into structured internal mental states:

Beliefs - the agent's understanding of the current state of the world based on observations and history

Desires - the goals the agent wants to achieve

Intentions - the committed plans of action the agent has decided to pursue

This isn't just philosophical - it's practical architecture. Instead of drowning in raw conversational data, the agent maintains a structured representation of what matters.

Quick Start

Here's how we integrate Agent Skills into our workflow:

# Agent Skills are static instruction sets
# Load them into your agent's system prompt or tools

# Example: Context Compression Skill
SYSTEM_PROMPT = """
You are an AI agent with context management skills.

When the conversation exceeds 80% of context capacity:
1. Identify high-signal information (user goals, key decisions, active tasks)
2. Compress or archive low-signal information (greetings, acknowledged confirmations)
3. Maintain a compact belief state of what matters

Belief State Format:
- User Goal: [primary objective]
- Active Tasks: [list of in-progress items]
- Key Decisions: [important choices made]
- Context Budget: [tokens used / tokens available]
"""

# The agent now actively manages its attention budget

A Real Example - Multi-Agent Orchestration

Let's say we're building a system where multiple specialized agents collaborate on a software development task:

# Orchestrator Agent with Context Engineering
from agent_framework import Agent, ContextManager

# Define specialized agents
researcher = Agent(
role="Technical Researcher",
skills=["context_compression", "belief_maintenance"]
)

coder = Agent(
role="Code Generator",
skills=["context_optimization", "dependency_tracking"]
)

reviewer = Agent(
role="Code Reviewer",
skills=["context_filtering", "quality_assessment"]
)

# Orchestrator manages context flow between agents
orchestrator = Agent(
role="Orchestrator",
skills=["multi_agent_coordination", "context_routing"],
agents=[researcher, coder, reviewer]
)

# The orchestrator maintains a shared context graph
# Each agent only receives relevant subsets
result = orchestrator.execute(
task="Build authentication system",
context_strategy="graph_based_routing"
)

# Each agent gets curated context, not everything
# Researcher gets: requirements, tech stack research
# Coder gets: architecture decisions, implementation guidelines
# Reviewer gets: code changes, quality criteria

Key Features

Context Compression Strategies - algorithms that identify and preserve high-signal tokens while compressing or discarding low-signal information. Think of it like ZIP compression, but semantic - keeping meaning while reducing tokens.

Multi-Agent Orchestration Patterns - blueprints for Orchestrator and Peer-to-Peer architectures where multiple agents collaborate with managed context sharing. Instead of every agent seeing everything, each agent receives only the context subset relevant to its role.

Memory Systems Architecture - graph-based and append-only log structures that persist important context across sessions. Like a distributed database for agent knowledge, not just flat message history.

Platform Agnostic Skills - instruction sets that work with Claude Code, Cursor, or any custom agent implementation. We're not locked into a specific vendor or framework.

BDI Cognitive Models - structured mental states (Beliefs, Desires, Intentions) that replace unstructured context accumulation. The agent knows not just WHAT information it has, but WHY it matters.

When to Use Agent Skills vs. Standard Prompt Engineering

Standard prompt engineering works great for single-shot tasks or short conversations where context management isn't critical. We use it when we need to tell an agent what to do and the entire task fits comfortably in one interaction.

Context Engineering becomes essential when we're building:

Long-running agent sessions that span hundreds of messages

Multi-agent systems where context needs to flow between specialized agents

Production applications where agents maintain state across user sessions

Complex workflows where the agent needs to make decisions based on accumulated knowledge

Other tools like LangChain or Semantic Kernel provide excellent frameworks for building agent applications, but they don't systematically address context curation as a first-class concern. Agent Skills complements these frameworks by providing the attention management layer they're missing.

My Take - Will I Use This?

In my view, this framework addresses the single biggest gap in production agent systems - nobody's been treating context management as a first-class engineering discipline. We've been so focused on model capabilities that we forgot to teach agents how to manage their own attention.

I'm particularly impressed by the BDI cognitive architecture implementation. It's not just academic theory - it's a practical pattern for structuring how agents maintain state. Instead of dumping everything into system prompts and hoping for the best, we now have a framework for teaching agents to curate deliberately.

The platform-agnostic approach is smart. Whether we're using Claude Code, Cursor, or building custom implementations, these Agent Skills remain relevant. That's rare in a space where every vendor wants to lock us into their ecosystem.

The main limitation is complexity - implementing these patterns requires more upfront design than simple prompt engineering. But for production systems where agent coherence matters, that investment pays off quickly. I'll definitely be incorporating these Context Engineering principles into our multi-agent workflows.

Check out the full framework: Agent Skills for Context Engineering

Frequently Asked Questions

What is Agent Skills for Context Engineering?

Agent Skills for Context Engineering is a modular framework that establishes Context Engineering as a distinct discipline for managing AI agent attention budgets across system prompts, tools, history, and documents.

Who created Agent Skills for Context Engineering?

Agent Skills for Context Engineering was created by Muratcan Koylan. The framework provides platform-agnostic instruction sets for implementing cognitive architectures in AI agents.

When should we use Context Engineering vs. standard prompt engineering?

Use Context Engineering when building long-running agent sessions, multi-agent systems, or production applications where maintaining coherent state across hundreds of interactions matters more than single-shot task completion.

What are the alternatives to Agent Skills?

Frameworks like LangChain and Semantic Kernel provide agent-building infrastructure but don't systematically address context curation. Agent Skills complements these by adding the attention management layer they're missing.

What are the limitations of Context Engineering?

The main limitation is increased architectural complexity compared to simple prompt engineering. It requires upfront design of cognitive models and context management strategies, which may be overkill for simple single-shot tasks.