Designing Context Systems for Autonomous AI Agents
AI agents represent a fundamental shift from single-turn interactions to multi-step, autonomous systems that can plan, execute, and adapt in real-time. Unlike traditional chatbots that respond to individual prompts, agents must maintain coherent context across dozens or hundreds of steps while coordinating multiple tools, recovering from errors, and tracking progress toward complex goals.
847%
Increase in agent-based AI deployments
The shift from simple chatbots to autonomous agents represents the fastest-growing segment of AI application development.
Key Insight
Agents Are Context Management Machines
The fundamental difference between a chatbot and an agent isn't capability—it's context architecture. While a chatbot processes each message in relative isolation, an agent must maintain and evolve context across planning, execution, observation, and reflection phases.
Framework
The OODA Loop for Agent Context
Observe Context
Raw information from tool outputs, environment state, and user inputs. This context should be factua...
Orient Context
Interpreted meaning of observations relative to goals. This is where the agent synthesizes what obse...
Decide Context
Available options, their tradeoffs, and selection rationale. This context explicitly lists 2-4 possi...
Act Context
Execution parameters, expected outcomes, and rollback procedures. Before any action, this context sp...
Chatbot vs. Agent Context Architecture
Traditional Chatbot Context
Single conversation history as linear sequence
Context window filled with raw message exchanges
No explicit goal tracking or progress measurement
Tools described once in system prompt, never updated
Hierarchical summarization with full-fidelity recent events
Explicit goal state with measurable progress indicators
Dynamic tool context based on current task phase
A
Adept AI
Building ACT-1's Multi-Stream Context System
Task completion rates jumped from 34% to 89% on complex workflows. Average steps...
Key Insight
The 'Goldfish Problem' in Agent Memory
Without careful context engineering, agents suffer from what researchers call the 'goldfish problem'—they forget what they were doing every few steps. This manifests as repeated actions, circular reasoning, and abandoned subtasks.
State Synchronization Is Your Biggest Risk
When agent state becomes inconsistent—goal says one thing, progress says another—agents exhibit bizarre behavior that's nearly impossible to debug. Implement state validation checks before every planning phase.
Tool Descriptions Are Agent Instructions in Disguise
The way you describe tools to an agent determines how and when they'll be used. Vague tool descriptions lead to misuse, overuse, or complete avoidance.
Anti-Pattern: The 'Kitchen Sink' Tool Description
❌ Problem
Agents become confused about when to use tools, often avoiding well-described to...
✓ Solution
Create layered tool descriptions with a core description (50 words max) that cov...
Crafting Production-Grade Tool Descriptions
1
Start with the Trigger Condition
2
Define the Core Action in One Sentence
3
Specify Input Requirements with Examples
4
Document the Output Structure
5
Add Explicit Anti-Patterns
S
Stripe
Redesigning Tool Descriptions for Their Support Agent
Tool selection errors dropped from 23% to 4.7%. Average task completion time dec...
Production Tool Description Templatejson
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{
"name": "get_order_status",
"trigger": "When user asks about order status, delivery time, or package location",
"description": "Retrieves current status and tracking information for a customer order",
"inputs": {
"order_id": {
"type": "string",
"required": true,
"format": "ORD-XXXXX (9 characters, alphanumeric after prefix)",
"example": "ORD-A1B2C",
"validation": "Must match regex ^ORD-[A-Z0-9]{5}$"
},
Test Tool Descriptions with Adversarial Prompts
Before deploying tool descriptions, test them with deliberately ambiguous requests. Ask 'I need to check something about order ORD-12345' and verify the agent selects the right tool.
Key Insight
Dynamic Tool Context Beats Static Tool Lists
Production agents shouldn't see all available tools all the time. Showing 50 tools when only 5 are relevant creates selection confusion and wastes context space.
Framework
Tool Context Lifecycle Management
Discovery Phase
Agent is understanding the task. Available tools: information retrieval, user clarification, context...
Planning Phase
Agent is formulating approach. Available tools: all read tools plus planning aids (create_plan, esti...
Execution Phase
Agent is carrying out the plan. Available tools: all tools relevant to current subtask. Use progress...
Recovery Phase
Entered after errors or unexpected results. Available tools: diagnostic tools, rollback tools, alter...
Tool Description Quality Audit
Agent Context Flow Architecture
User Input
Goal Parser (extract...
Planner (receives go...
Tool Selector (recei...
Practice Exercise
Audit and Improve Your Tool Descriptions
45 min
Framework
The TRACE Framework for Agent Context
Task Definition
Clear articulation of the agent's current objective, success criteria, and constraints. This include...
Resources Available
Complete inventory of tools, APIs, data sources, and capabilities the agent can leverage. Each resou...
Action History
Compressed log of actions taken, their outcomes, and lessons learned. Rather than raw transcripts, t...
Current State
Real-time snapshot of the agent's position in the task, including progress indicators, accumulated r...
Structured Tool Description Schematypescript
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interface ToolDescription {
name: string;
category: 'data' | 'action' | 'analysis' | 'communication';
description: {
purpose: string; // What it does (1-2 sentences)
useWhen: string[]; // Conditions favoring this tool
avoidWhen: string[]; // Conditions against this tool
alternatives: string[]; // Related tools for comparison
};
parameters: {
name: string;
type: string;
Stateless vs. Stateful Agent Context
Stateless Agent Design
Full context reconstructed each turn from conversation histo...
Simpler architecture with no external state management
Natural fit for serverless deployments and horizontal scalin...
Context grows linearly with conversation length, hitting lim...
Stateful Agent Design
Persistent state store maintains compressed agent memory
Complex architecture requiring state management infrastructu...
Requires sticky sessions or distributed state synchronizatio...
Context remains bounded regardless of conversation length
A
Anthropic
Claude's Computer Use Agent State Management
The tiered approach enabled Claude to complete tasks requiring 200+ sequential a...
Key Insight
Planning Context Is About Constraining, Not Expanding Options
Many engineers assume planning context should expose all possible actions to the agent, letting it choose freely. This is backwards.
Framework
The Planning Horizon Framework
Immediate Context (Next Action)
What the agent needs to decide its very next step. Includes current state, available tools, and imme...
Tactical Context (Next 3-5 Actions)
Short-term plan providing direction without over-specifying. Includes current sub-goal, success crit...
Strategic Context (Overall Task)
High-level task understanding that persists throughout execution. Includes original objective, key c...
Historical Context (Lessons Learned)
Compressed wisdom from previous actions in this task. Not a raw log, but extracted insights: 'API X ...
Anti-Pattern: The Infinite Retry Loop
❌ Problem
Users experience hung agents that burn through API credits attempting the same f...
✓ Solution
Implement structured error context that captures failure type, attempted recover...
Improvement in multi-step task completion with scratchpad patterns
Research on agent reasoning showed that providing explicit scratchpad space improved completion rates on tasks requiring 10+ reasoning steps from 34% to 89%.
Key Insight
Agent State Should Be Queryable, Not Just Readable
Most agent implementations treat state as a static blob injected into every prompt. Advanced implementations make state queryable—the agent can ask specific questions about its state rather than receiving everything.
Agent Context Architecture Review
N
Notion
Notion AI's Progressive Context Narrowing
Tool selection accuracy improved from 71% to 94%, and user satisfaction scores i...
Agent Context Flow Architecture
User Request
Intent Classificatio...
Context Assembly (Ta...
Planning Phase (Narr...
Context Isolation Prevents Contamination
When agents handle multiple concurrent tasks or serve multiple users, strict context isolation is essential. A common bug: state from one user's session leaks into another's context, causing bizarre behavior or privacy violations.
Practice Exercise
Design an Agent Context Schema
45 min
Framework
The CLEAR Error Recovery Framework
Classify
Categorize errors by recoverability: transient (retry likely to succeed), permanent (retry will fail...
Log
Record error details in structured format: error type, tool involved, inputs that caused failure, ti...
Evaluate
Assess recovery options against current task state. What fallbacks exist? What would we lose by usin...
Act
Execute the chosen recovery strategy: retry with backoff, try alternative approach, request user inp...
Agents with 50+ tools in context show 40% lower task completion rates than agent...
✓ Solution
Implement dynamic tool loading based on task type and phase. Start with a core s...
Anti-Pattern: The Amnesiac Agent
❌ Problem
Agents repeat failed approaches because they don't remember what didn't work. Th...
✓ Solution
Implement a structured scratchpad that persists key observations, decisions, and...
Anti-Pattern: The Overconfident Planner
❌ Problem
Teams report that 60% of detailed upfront plans require significant revision aft...
✓ Solution
Use rolling horizon planning: create detailed plans for the next 3-5 steps with ...
Practice Exercise
Error Recovery Scenario Testing
45 min
Practice Exercise
Scratchpad Optimization Challenge
60 min
Dynamic Tool Loading Systempython
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from typing import List, Dict, Optional
from dataclasses import dataclass
@dataclass
class ToolCategory:
name: str
description: str
tools: List[str]
load_triggers: List[str] # Keywords that suggest this category
class DynamicToolLoader:
def __init__(self, all_tools: Dict[str, ToolDescription]):
Essential Resources for Agent Context Engineering
ReAct: Synergizing Reasoning and Acting in Language Models
article
LangChain Agent Documentation
tool
AutoGPT Architecture Analysis
article
Anthropic's Tool Use Documentation
article
Version Your Agent Context Like Code
Treat your agent context templates as code artifacts with proper version control, code review, and testing. When you modify tool descriptions or scratchpad formats, run regression tests against a suite of representative tasks.
Watch for Context Drift in Long Sessions
Agents running for many turns can experience 'context drift' where accumulated scratchpad content gradually shifts the agent's behavior away from its original purpose. Implement periodic context resets that reaffirm the agent's core identity and current goal, especially after every 10-15 turns or when switching between task phases..
Human Oversight is Non-Negotiable
No matter how sophisticated your agent context, always include clear escalation paths and approval gates for high-impact actions. The most reliable agents are those that know when to stop and ask for help.
Framework
The TRACE Framework for Agent Debugging
Tools
Examine tool descriptions for ambiguity, missing parameters, or unclear failure modes. Check if the ...
Reasoning
Review the scratchpad and planning context. Did the agent have enough information to reason correctl...
Actions
Analyze the sequence of tool calls. Were parameters correct? Did the agent interpret results accurat...
Context
Evaluate the full context sent to the model. Was critical information buried or missing? Did context...
85%
of agent failures are context failures, not model failures
Analysis of 10,000 failed agent runs showed that the vast majority of failures could be attributed to inadequate context: missing tool descriptions, stale state, insufficient error context, or ambiguous instructions.
Chapter Complete!
Tool descriptions are the foundation of agent capability—inv...
Agent state context must be actively managed, not passively ...
Planning context should balance strategic thinking with adap...
Error recovery context transforms brittle agents into resili...
Next: Start by auditing your existing agent's tool descriptions against the quality criteria in this chapter—most teams find immediate improvement opportunities