Long-Running AI Agents and Task Decomposition 2026

Executive Summary

2026 marks a pivotal transition in AI agent capabilities from short-interaction chatbots to long-horizon systems capable of autonomous work spanning hours, days, or even weeks. Current data shows AI task duration doubling every 7 months, with agents now handling 2-hour tasks autonomously and projections showing 8-hour workdays by late 2026, full work weeks (40 hours) by 2028, and work months (167 hours) by 2029.

This research examines the architectural patterns, operational challenges, and production best practices that enable these extended operations. Key findings include:

Exponential capability growth: Task completion length doubles every 7 months, but doubling task duration quadruples the failure rate
Planner-Worker architecture dominance: 90% cost reduction possible by using capable models for planning and cheaper models for execution
Context management crisis: Every agent experiences performance degradation after 35 minutes of human time, with context drift being a fundamental challenge
Production adoption surge: Enterprise AI agent adoption growing from 5% (early 2025) to projected 40% (end of 2026)
Real-world validation: Systems like Devin have merged hundreds of thousands of PRs at companies like Goldman Sachs, achieving 20% efficiency gains

The transition from "fast AI" (instant responses) to "slow AI" (minutes to hours) requires fundamental UX pattern changes, robust state management, sophisticated error recovery, and new cost optimization strategies.

1. The Long-Horizon Agent Revolution

1.1 Moore's Law for AI Agents

Research from METR demonstrates an exponential growth curve in AI agent task completion capabilities. The length of coding tasks that frontier systems can complete is doubling every 7 months, creating what some experts are calling "a new Moore's Law for AI agents."

Capability Timeline:

Early 2025: 1-hour tasks
2026: 2-hour tasks (current)
Late 2026: 8-hour workdays
2028: 40-hour work weeks
2029: 167-hour work months

However, this growth comes with a critical caveat: tasks requiring longer durations necessitate more stages, and doubling the task duration quadruples the failure rate. This non-linear relationship between task duration and failure probability is a fundamental challenge in agent design.

1.2 What Defines a Long-Horizon Agent?

Long-horizon agents are characterized by:

Multi-session operation: Work spans multiple context windows, requiring state preservation across sessions
Autonomous decision-making: Thousands of independent decisions without human intervention
Persistent memory: Recall and build upon previous work across hours or days
Failure recovery: Ability to detect errors, backtrack, and retry without starting over
Progress tracking: Maintain awareness of what's been completed and what remains

Industry experts increasingly consider long-horizon agents as functionally equivalent to AGI for practical purposes, as they can complete work indistinguishable from human output across extended timeframes.

1.3 The Performance Degradation Problem

Research reveals a critical limitation: every AI agent experiences performance degradation after 35 minutes of human time spent on a task. This represents a fundamental challenge as agents scale from short interactions to extended operations.

The core issues driving degradation include:

Context window limitations: Even with 200K+ token windows, complex projects exceed capacity
Attention decay: Model performance decreases as context fills with prior decisions
Compounding errors: Small mistakes early in a task cascade into larger problems
State management complexity: Tracking progress across discrete sessions becomes exponentially harder

2. Task Decomposition Architectures

2.1 Planner-Worker Pattern (Dominant Architecture)

The Planner-Worker model has emerged as the dominant architecture for long-running agents, adopted by leading systems including:

Cursor (with GPT-5.2)
AWS Strands and ADK
Claude Code
Most agentic IDEs

Architecture:

┌─────────────────────────────────────┐
│  Planner (Frontier Model)           │
│  - High-level reasoning             │
│  - Task decomposition               │
│  - Strategy creation                │
│  - Quality assurance                │
└──────────────┬──────────────────────┘
               │
               ▼
    ┌──────────────────────────┐
    │  Task Queue              │
    └──────────┬───────────────┘
               │
     ┌─────────┴─────────┐
     ▼                   ▼
┌─────────┐         ┌─────────┐
│ Worker  │   ...   │ Worker  │
│ (Cheap  │         │ (Cheap  │
│ Model)  │         │ Model)  │
└─────────┘         └─────────┘

Cost Economics:

Capable model creates strategy once
Cheaper models execute repetitive tasks
Cost reduction: up to 90% compared to using frontier models for everything

Example Decomposition:

High-level goal: "Reconcile Q4 financial records"

Planner breaks down into:
├── Download bank statements
├── Extract transaction data
├── Compare with internal ledger
├── Flag discrepancies
└── Generate reconciliation report

Workers execute each atomic task

2.2 Hierarchical Planning Modules

Hierarchical Planning Modules extend the Planner-Worker pattern by creating tree-like structures of sub-tasks and atomic actions. This approach is particularly effective for complex, multi-stage processes.

Key Features:

Nested decomposition: Tasks break down recursively into smaller units
Dependency tracking: Understanding which tasks must complete before others
Parallel execution: Independent sub-tasks run simultaneously
Context isolation: Each sub-task operates in limited context, reducing drift

Production Framework: AgentOrchestra

AgentOrchestra exemplifies hierarchical planning with:

Planning Agent: Central orchestrator for high-level reasoning and adaptive planning
Specialized Sub-Agents: Assigned tasks based on expertise and evolving context
Dynamic reallocation: Tasks shift between agents as context evolves

2.3 Multi-Agent Collaboration

Single-task reasoning is evolving into multi-agent coordination where systems collaborate on 8+ hour workflows. The pattern involves:

Specialized Agent Roles:

Researcher: Gathers information and analyzes requirements
Writer: Produces code, documentation, or content
Reviewer: Quality assurance and validation
Integrator: Combines outputs and resolves conflicts

Coordination Challenges:

Interdependent ecosystems: Multi-agent systems carry potential for compounding errors
Communication overhead: Agents must share context efficiently
Conflict resolution: Disagreements between agents require resolution mechanisms
Synchronization: Ensuring agents work on consistent state

2.4 The "Deep Agents" Architecture (Agents 2.0)

A new paradigm called "Deep Agents" represents the evolution to Agents 2.0, featuring four foundational pillars:

Explicit Planning:
- Pre-planned sequences of actions
- Clear decision trees and branching logic
- Predictable execution paths
Hierarchical Delegation:
- Task routing to specialized sub-agents
- Depth-first task execution
- Clear responsibility boundaries
Persistent Memory:
- Long-term storage across sessions
- Context retrieval on-demand
- Learning from past interactions
Extreme Context Engineering:
- Context compaction strategies
- State offloading to external storage
- Task isolation to manage context windows

This architecture directly addresses the "35-minute degradation problem" by breaking long tasks into manageable chunks that fit within the effective performance window.

3. Context Management for Extended Operations

3.1 The Context Management Crisis

Getting agents to make consistent progress across multiple context windows remains an open problem in 2026. The fundamental challenge: agents must work in discrete sessions, with each new session beginning with no memory of what came before.

Technical Constraints:

Context windows limited (even 200K tokens insufficient for week-long projects)
Linear token costs make naive context accumulation economically unfeasible
Model performance degrades as context fills (attention decay)
Critical information gets "lost in the middle" of long contexts

3.2 Context Management Techniques

1. Context Editing (Pruning)

Intelligently dropping or summarizing stale content from prompts:

Selective retention: Keep only decision-critical information
Summarization: Compress completed tasks into brief summaries
Recency bias: Prioritize recent context over historical
Result: 100+ turn conversations using fewer total tokens

2. External Memory Systems

Function calling to access real databases instead of storing in context:

Persistent storage: Save state to databases, file systems, or key-value stores
On-demand retrieval: Load only relevant information when needed
Structured formats: JSON, SQL, or document databases for organized access
Search capabilities: Vector search or full-text search for context retrieval

3. Thought Signatures and State Tracking

Mechanisms to maintain reasoning state across sessions:

Decision logs: Record why choices were made
Checkpoint metadata: Save reasoning state at key milestones
Thought chains: Link current reasoning to previous decisions
Progress markers: Track completion percentage and remaining work

4. Hierarchical Context Isolation

Breaking tasks into independent sub-tasks with isolated context:

Sub-agent delegation: Each worker operates in fresh context
Parent-child coordination: Parent maintains high-level state, children handle details
Context boundaries: Clear interfaces between hierarchical levels
Reduced drift: Isolated contexts prevent error propagation

3.3 Extreme Context Engineering

Advanced strategies for managing context in production systems:

Token Budget Management:

Monitor token consumption per interaction
Set hard limits on context accumulation
Trigger compaction when approaching limits
Alert systems when budgets risk being exceeded

Strategic Caching:

Cache common agent responses and patterns
Reduce redundant context regeneration
Share cached context across similar tasks
Result: Orders of magnitude reduction in token usage

Tool Output Management:

Anti-pattern: Funneling large tool outputs through the model
Best practice: Load only tools needed for current sub-task
Result: Orders of magnitude drop in token consumption, faster execution, sidesteps context limits

4. Real-World Production Deployments

4.1 Cursor: Week-Long Autonomous Runs

Background:

Raised $2.3B Series D (December 2025)
Passed $1B in annualized revenue
Primary AI coding IDE for developers

GPT-5.2 Integration:

Released December 11, 2025
Described as "most advanced frontier model for professional work and long-running agents"
Explicitly designed for extended autonomous operations

Week-Long Agent Capabilities:

"We've been experimenting with running coding agents autonomously for weeks at a time"
Engineers use Background Agents for independent, parallel long-running tasks
Support for parallel foreground agents when switching between different tasks
Review outputs across multiple concurrent agents

Production Patterns:

Background Agents: Run independently while user works on other tasks
Parallel Execution: Multiple agents tackle different aspects simultaneously
Context Switching: Switch between agents without losing progress
Review Workflows: Human-in-the-loop validation at key milestones

4.2 Devin: Enterprise AI Software Engineer

Performance Metrics (18 months in production):

Merged PRs: Hundreds of thousands
Speed: 4x faster at problem solving (year-over-year)
Efficiency: 2x more efficient in resource consumption
Merge rate: 67% of PRs merged (vs 34% in first year)
Pricing: Reduced from $500/month to $20/month (Core plan, April 2025)

Enterprise Adoption:

Deployed at Goldman Sachs (12,000 human developers)
Santander and Nubank production usage
Goldman Sachs CIO reports 20% efficiency gains
"Hybrid workforce" model with humans and agents

Long-Horizon Capabilities:

Context maintenance: Maintains context across long-running tasks
Learning: Learns from interactions over time
Complex planning: Executes tasks requiring thousands of decisions
Context recall: Recalls relevant context at every step (multi-file refactoring example)
Self-correction: Fixes mistakes and adapts approach

2026 Focus Areas:

Better understanding of real-world codebases
Enhanced context utilization for end-to-end collaboration
UX improvements for directing everyday development
Memory enhancements for long-term projects

4.3 Enterprise Adoption Trends

Gartner Projections:

Early 2025: <5% of enterprise applications with embedded AI agents
End 2026: 40% of enterprise applications with embedded AI agents
Growth rate: 8x increase in 18 months

Industry Sectors Leading Adoption:

Financial Services: Goldman Sachs, Santander, Nubank
Customer Service: 92% of brands using AI-driven personalization with 24/7 support
Software Development: GitHub Copilot, Cursor, Devin widespread
Retail: AI agents for inventory, personalization, logistics

Production Use Cases:

Multi-day customer support cases
Week-long software development projects
Extended research and analysis tasks
Continuous monitoring and response systems

5. Operational Challenges

5.1 Error Recovery and Resilience

Core Challenge: Research shows every agent experiences success rate decrease after 35 minutes, and doubling task duration quadruples failure rate. This makes error recovery critical for long-horizon tasks.

Recovery Strategies:

1. Stateful Recovery

Persistent storage: Save agent state and context at regular intervals
Last known good state: Enable resumption from checkpoints after failures
State reconstruction: Rebuild agent state from persisted data
Result: Agents survive restarts, crashes, and timeouts

2. Git-Based Recovery

Version control integration: Commit work at logical checkpoints
Revert capability: Use git to undo bad code changes
State comparison: git diff to identify what changed when errors occur
Efficiency gain: Eliminates need for agents to guess what went wrong

3. Validation and Testing

Major failure mode: Agents marking features complete without testing
Best practice: Explicit prompting to use browser automation and test as humans would
Dramatic improvement: Proper testing requirements significantly improve reliability
Layered validation: Deterministic validators + LLM evaluation + human oversight

4. Retry Logic with Backoff

Progressive retry with increasing delays
Circuit breakers to prevent infinite loops
Alternative approach generation after repeated failures
Human escalation when retry threshold exceeded

5.2 Token Costs and Economic Viability

The Token Cost Crisis:

Baseline Economics:

GPT-4 Turbo: ~$0.01-$0.03 per 1,000 tokens
Mid-sized product (1,000 daily users): 5-10 million tokens/month
Cost volatility: Minor prompt changes can double costs overnight

Real-World Cost Challenges:

Retries multiply costs: Each failed attempt consumes tokens
Longer contexts: Extended operations require more context, increasing input token costs
Multi-step reasoning: Complex tasks may require multiple model calls per decision
Tool usage: Function calling adds overhead to each interaction

Cost Governance Crisis:

Only 15% can forecast AI costs within ±10%
84% of companies report AI costs cutting gross margins by >6%
Even minor changes can spike costs 100x overnight
Token-based pricing fluctuates unpredictably with usage patterns

Cost Optimization Strategies:

1. Planner-Worker Pattern

Use expensive model for planning once
Cheap models execute repetitive tasks
90% cost reduction demonstrated in production

2. Strategic Caching

Cache common agent responses
Share cached context across similar tasks
Reduce redundant prompt regeneration

3. Token Budget Monitoring

Real-time tracking of token consumption
Alerts when approaching budget limits
Automatic context compaction triggers
Per-feature cost attribution

4. Tool Output Management

Avoid passing large tool outputs through model
Use function calling to access data directly
Load only needed tools for current sub-task
Orders of magnitude reduction in token consumption

5. Structured Outputs

Constrained generation reduces token waste
JSON mode ensures parseable responses
Function calling provides predictable formats
Reduces retry loops from malformed outputs

5.3 The "Slow AI" UX Challenge

The transition from instant responses (fast AI) to responses taking minutes or hours (slow AI) requires fundamental UX pattern changes.

New UX Requirements:

1. Goal Clarification and Confirmation

Explicit goal definition before long runs
User confirmation of approach
Cost and time estimates upfront
Clear success criteria

2. Progress Transparency

ETA ranges with confidence levels
Intermediate results as they're produced
Real-time status updates
Percentage completion indicators

3. Intervention Capabilities

Ability to pause long-running operations
Mid-execution adjustments without restart
Cancel with partial result preservation
Steering corrections when agent drifts

4. Asynchronous Workflows

Background execution while user does other work
Notification system for completion
Result review interfaces
Approval gates at key milestones

5. 24/7 Operations

Agents work overnight without supervision
Morning briefings on overnight progress
Error alerts requiring human intervention
Continuous operation with periodic check-ins

6. State Management and Checkpointing

6.1 The Checkpointing Revolution

2026 has seen significant maturation in persistent state management for extended tasks. Modern frameworks now provide automatic state preservation across interruptions.

Key Technologies:

LangGraph:

Robust checkpointing with persistent memory states
Safe parallel task execution
PostgresSaver for data integrity during restarts
Every state change automatically checkpointed

Microsoft Agent Framework:

Server-side checkpointing for long-running processes
Durable storage enabling distributed execution
Messages, tool calls, and decisions all checkpointed
Recovery and resumption across multiple instances

Production Benefits:

Multi-day conversations: Context preserved across days/weeks
Process restarts: Survive deployments and crashes
Distributed execution: Move between instances seamlessly
Audit trails: Complete history of agent decisions

6.2 Agent Harness Infrastructure

An Agent Harness is the infrastructure wrapping an AI model to manage long-running tasks. It's not the agent itself, but the operational layer enabling extended execution.

Harness Responsibilities:

1. Context Engineering

Context compaction strategies
State offloading to external storage
Task isolation into sub-agents
Token budget management

2. State Preservation

Automatic checkpointing at key points
State serialization and deserialization
Database integration for persistence
Recovery from last good checkpoint

3. Progress Tracking

Task completion percentage
Sub-task status monitoring
Dependency graph management
Estimated time to completion

4. Error Handling

Exception capture and logging
Automatic retry with backoff
Alternative approach generation
Human escalation triggers

5. Resource Management

Token budget enforcement
Rate limiting and throttling
Parallel execution coordination
Priority queue management

6.3 Memory Systems for Long-Horizon Tasks

Persistent Memory Requirements:

1. Conversation History

Complete record of agent interactions
Searchable message archive
Context retrieval on-demand
Summarization for older history

2. Task Context

Current goal and sub-goals
Progress on each sub-task
Decisions made and rationale
Blockers and dependencies

3. Learning and Adaptation

Patterns that work for similar tasks
User preferences and style
Error patterns to avoid
Successful approaches to reuse

4. State Snapshots

Full agent state at checkpoints
Rollback capability to any snapshot
Branch and merge for parallel exploration
Time-travel debugging

Production Frameworks:

LangGraph: PostgresSaver for durable checkpoints
Microsoft Agent Framework: Durable Task Extension
Custom solutions: Redis, PostgreSQL, or specialized vector databases
Hybrid approaches: Hot state in memory, cold state in database

7. Production Best Practices

7.1 Defense-in-Depth for Long-Horizon Tasks

Production-grade agents require layered protections combining multiple safety mechanisms:

1. Deterministic Validators

Syntax checking before execution
Type validation for structured outputs
Business rule enforcement
Security policy compliance

2. LLM-Based Evaluation

Semantic correctness checking
Output quality assessment
Alignment with goals verification
Confidence scoring

3. Human Oversight

Approval gates at critical milestones
Review workflows for high-risk actions
Exception handling escalation
Final validation before delivery

4. Comprehensive Observability

Real-time monitoring of agent operations
Logging all decisions and actions
Performance metrics tracking
Cost attribution and alerting

7.2 Modular and Scalable Design

Architectural Principles:

1. Modularity

Clear separation of concerns
Well-defined interfaces between components
Pluggable sub-agents for specialized tasks
Independent scaling of components

2. Flexibility

Configuration-driven behavior
Easy to add new capabilities
Adaptable to different task types
Support for multiple LLM providers

3. Scalability

Horizontal scaling of worker agents
Database sharding for large-scale state
Distributed execution across regions
Queue-based task distribution

7.3 Testing and Validation

Critical Success Factor: A major failure mode is agents marking features complete without proper testing. This is consistently identified as the top reliability issue.

Best Practices:

1. Explicit Testing Requirements

Prompt engineering to require testing
Browser automation tools for UI validation
Test-as-human-would approach
Automated test generation

2. Multi-Layer Testing

Unit tests for individual components
Integration tests for workflows
End-to-end tests for complete tasks
Regression tests for known failure modes

3. Validation Metrics

Test coverage requirements
Pass rate thresholds
Performance benchmarks
Quality gates before merge

7.4 Security and Governance

2026 Security Requirements:

1. Start Narrow

Well-defined tasks with limited scope
Contained "blast radius" for failures
Gradual expansion as confidence grows
Learn from web security evolution, not repeat mistakes

2. Layered Security

Input validation and sanitization
Output filtering for sensitive data
Sandboxed execution environments
Audit logging of all actions

3. Governance Framework

Clear policies for agent behavior
Approval workflows for sensitive operations
Compliance with regulations (GDPR, SOC2, etc.)
Regular security audits

7.5 Performance Metrics and KPIs

Measurable Success Criteria:

1. Accuracy Rates

Target: ≥95% for production systems
Measured against human-validated ground truth
Tracked per task type
Trended over time

2. Task Completion Rates

Target: ≥90% for production systems
Percentage of tasks completed without human intervention
Time-to-completion distributions
Blockers and failure mode analysis

3. Response Times

P50, P95, P99 latencies
Time-to-first-token for streaming
End-to-end task duration
Comparison to human baseline

4. Business Impact

Cost savings vs. human labor
Productivity improvements (e.g., Goldman's 20%)
Customer satisfaction scores
Revenue impact attribution

5. Cost Metrics

Cost per task
Token consumption per interaction
Cost per successful outcome
ROI calculation

7.6 Data Pipeline Quality

Critical Infrastructure: Data pipeline failures are one of the most prevalent causes of AI agents operating incorrectly in production.

Requirements:

1. Real-Time Access

Low-latency data retrieval
Fresh data for decision-making
Streaming updates for live systems
Cache invalidation strategies

2. Quality Validation

Schema validation for structured data
Data freshness checks
Completeness verification
Anomaly detection

3. Seamless Integration

Native database connectors
API integration with error handling
Message queue integration
Event-driven architectures

7.7 Framework Selection Guide

For Teams Starting Agent Development:

Recommended Entry Points:

CrewAI: Best balance of capability and approachability
LangChain: Extensive ecosystem and community
Advantages: Lower learning curve, rapid prototyping, community support

For Mature Production Systems:

Advanced Frameworks:

LangGraph: State machine-based workflows, checkpointing
AutoGen: Multi-agent collaboration, complex orchestration
Microsoft Agent Framework: Enterprise integration, durable execution
Advantages: Advanced features for complex production scenarios

Selection Criteria:

Team expertise and learning curve
Integration requirements (databases, APIs, enterprise systems)
Scale requirements (requests per day, concurrent agents)
Budget (framework costs, hosting costs, LLM costs)
Support needs (community vs. enterprise support)

8. The Path Forward: 2026 and Beyond

8.1 Current State of the Art

What Works Today (2026):

2-hour autonomous coding tasks
Multi-day customer support cases
Week-long development sprints with oversight
Hundreds of thousands of PRs in production
20% efficiency gains at enterprise scale

What's Still Challenging:

Consistent progress beyond 35 minutes without degradation
Cost predictability at scale
Error recovery without human intervention
Context management for truly long-horizon tasks (weeks+)
Multi-agent coordination without compounding errors

8.2 Near-Term Trajectory (2026-2028)

2026 Expectations:

8-hour work days by Q4 2026
40% enterprise adoption by end of year
$52B market by 2030 (from $7.8B today)
Mature checkpointing and state management
Cost optimization as first-class architectural concern

2027-2028 Projections:

Full work weeks (40 hours) by 2028
Multi-week projects becoming common
Hybrid human-agent workforces normalized
Agent-to-agent communication protocols standardized
Robust error recovery and self-correction

8.3 Research Challenges

Open Problems:

1. Consistent Multi-Window Progress

Maintaining quality across context window boundaries
Preventing performance degradation beyond 35 minutes
Context handoff between sessions

2. Cost Economics

Predictable cost forecasting
Cost optimization without quality degradation
Economic viability for smaller organizations

3. Compounding Error Prevention

Early error detection before propagation
Validation strategies that scale
Self-correction without human intervention

4. Multi-Agent Coordination

Efficient communication protocols
Conflict resolution mechanisms
Synchronized state management
Collective learning across agent swarms

5. Observability and Governance

Real-time monitoring of agent estates
Orchestration infrastructure for large-scale deployments
Compliance and audit requirements
Security frameworks for autonomous systems

8.4 The AGI Question

Long-Horizon Agents as Functional AGI:

Many experts consider long-horizon agents that can complete week-long tasks autonomously to be functionally equivalent to AGI for practical purposes. Key reasoning:

Indistinguishable output: Work product matches or exceeds human quality
Autonomous operation: Minimal supervision required
Complex problem-solving: Thousands of interdependent decisions
Adaptability: Handles unforeseen challenges and adjusts approach
Economic impact: Displaces human labor at meaningful scale

Counter-Arguments:

Still narrow domain-specific (e.g., coding, customer service)
Requires human-designed architecture and tooling
Cannot transfer learning across domains like humans
Lacks general world understanding and common sense
Fails at novel tasks outside training distribution

Sequoia Capital Perspective: "2026: This is AGI" - viewing long-horizon agents as the practical realization of artificial general intelligence, regardless of philosophical definitions.

9. Conclusion

Long-running AI agents represent a fundamental shift from reactive chatbots to proactive, autonomous systems capable of sustained work over hours, days, or weeks. The exponential growth in task duration capabilities (doubling every 7 months) is driving rapid adoption, with enterprise deployment projected to grow 8x from early 2025 to end of 2026.

Key Success Factors:

Architectural patterns: Planner-Worker and hierarchical decomposition enable cost-effective scaling
Context management: External memory, pruning, and isolation strategies overcome window limitations
State management: Checkpointing and agent harness infrastructure enable multi-day operations
Error recovery: Git-based rollback, validation layers, and retry logic ensure resilience
Cost optimization: Strategic caching, token budgets, and model selection achieve economic viability

Production Validation:

Real-world deployments like Devin (hundreds of thousands of merged PRs) and Cursor (week-long autonomous runs) demonstrate that long-horizon agents are production-ready for specific domains. Goldman Sachs' 20% efficiency gains and enterprise adoption surge validate the business case.

Remaining Challenges:

The "35-minute degradation problem," cost predictability, multi-agent coordination, and error compounding remain open research problems. However, the trajectory is clear: by 2028, agents handling full work weeks will be commonplace, and the distinction between human and agent knowledge workers will blur.

The question is no longer whether long-running AI agents are possible, but how quickly organizations can adapt their processes, culture, and infrastructure to collaborate with these new autonomous colleagues.