Agent Context Preservation for Long-Running Workflows: Checkpoints, Sub-Agent Delegation, and Avoiding Context Pollution

Calendar February 22, 2026
Agent-Tooling
Intermediate, Advanced
Context-Preservation, Checkpoint-Design, Sub-Agent-Delegation, Context-Scoping, Workflow-State-Management
Context-Management, Context-Preservation, Checkpoints, Sub-Agents, Context-Pollution, Long-Running, Todo-Lists, Claude-Code, Memory-Files, Skills, Spec-Documents, Agent-Delegation, Context-Window
Claude-Code, Filesystem, Git, Markdown

Agent Context Preservation for Long-Running Workflows#

The context window is the single most important constraint in agent-driven work. A single-turn task uses a fraction of it. A multi-hour project fills it, overflows it, and degrades the agent’s reasoning quality long before the task is complete. Agents that work effectively on ambitious projects are not smarter – they manage context better.

This article covers practical, battle-tested patterns for preserving context across long sessions, delegating to sub-agents without losing coherence, and avoiding context pollution – the gradual degradation that happens when irrelevant information accumulates in the working context.

Agent-Oriented Terraform: Linear Patterns for Machine-Managed Infrastructure

Calendar February 22, 2026
Infrastructure
Intermediate, Advanced
Terraform-Agent-Patterns, State-Decomposition, Linear-Terraform-Design, Agent-Oriented-Iac
Terraform, Agent-Patterns, Linear-Code, State-Decomposition, Parallelism, Modules, Agent-Oriented, Infrastructure-as-Code, Refactoring, Maintainability
Terraform, Claude-Code

Agent-Oriented Terraform#

Most Terraform code is written by humans for humans. It favors abstraction, DRY principles, and deep module nesting — patterns that make sense when a human maintains a mental model of the codebase. Agents do not maintain mental models. They read code fresh each time, trace references to resolve dependencies, and reason about the full resource graph in a single context window.

The patterns that make Terraform elegant for humans make it expensive for agents. Deep module nesting multiplies the files an agent must read. Variable threading through three layers of modules hides dependencies behind indirection. Complex for_each over maps of objects creates resources that are invisible until runtime. The agent spends most of its context on navigation, not comprehension.

Designing Agent-Ready Projects: Structure That Benefits Humans and Agents Equally

Calendar February 22, 2026
Agent-Tooling
Beginner, Intermediate
Project-Structuring, Documentation-Design, Convention-Definition, Onboarding-Optimization
Project-Structure, Agent-Ready, Documentation, Conventions, Claude-Md, Todo, Onboarding, Best-Practices, Developer-Experience, Project-Setup
Claude-Code, Filesystem, Git, Markdown

Designing Agent-Ready Projects#

An “agent-ready” project is just a well-documented project. Every practice that helps an agent — clear conventions, explicit commands, tracked progress, documented decisions — also helps a new team member, a future-you who forgot the details, or a contractor picking up the project for the first time.

The difference is that humans can ask follow-up questions and gradually build context through conversation. Agents cannot. They need it written down, in the right place, at the right level of detail. Projects that meet this bar are better for everyone.

How Agents Communicate: Explaining Plans, Risks, and Trade-offs to Humans

Calendar February 22, 2026
Agent-Tooling
Beginner, Intermediate
Plan-Communication, Risk-Explanation, Progress-Reporting, Escalation-Framing, Trade-Off-Presentation
Communication, Agent-Human-Interaction, Trust, Plan-Presentation, Risk-Communication, Trade-Offs, Progress-Reporting, Escalation, Transparency, Collaboration
Claude-Code, Markdown

How Agents Communicate#

The most capable agent in the world is useless if the human does not understand what it is doing, why it is doing it, or what it needs. Poor communication is the single largest cause of failed agent-human collaboration — not poor reasoning, not incorrect code, but the human losing confidence because the agent did not explain itself well enough.

This article covers communication patterns that build trust: how to present plans, explain risks, frame trade-offs, report progress, and escalate problems. It is written for agents to follow and for humans to know what good agent communication looks like.

Human-in-the-Loop Patterns: Approval Gates, Escalation, and Progressive Autonomy

Calendar February 22, 2026
Agent-Tooling
Intermediate
Approval-Gate-Design, Escalation-Policy, Risk-Classification, Autonomy-Calibration
Human-in-the-Loop, Approval-Gates, Escalation, Autonomy, Agent-Safety, Risk-Assessment, Destructive-Actions, Confirmation, Trust, Guardrails
Claude-Code, Mcp, Ci-Cd-Pipelines

Human-in-the-Loop Patterns#

The most common failure mode in agent-driven work is not a wrong answer – it is a correct action taken without permission. An agent that deletes a file to “clean up,” force-pushes a branch to “fix history,” or restarts a service to “apply changes” can cause more damage in one unauthorized action than a dozen wrong answers.

Human-in-the-loop design is not about limiting agent capability. It is about matching autonomy to risk. Safe, reversible actions should proceed without interruption. Dangerous, irreversible actions should require explicit approval. The challenge is building this classification into the workflow without turning every action into a confirmation dialog.

Long-Running Workflow Orchestration: State Machines, Checkpointing, and Resumable Multi-Agent Execution

Calendar February 22, 2026
Agent-Tooling
Advanced
Workflow-Orchestration, State-Machine-Design, Checkpoint-Strategy, Sub-Agent-Management, Failure-Recovery
Long-Running-Workflows, State-Machines, Checkpointing, Resumption, Sub-Agents, Parallel-Execution, Workflow-Orchestration, Failure-Recovery, Spec-Documents, Context-Window
Claude-Code, Filesystem, Git, Markdown, Task-Runners

Long-Running Workflow Orchestration#

Most agent examples show single-turn or single-session tasks: answer a question, write a function, debug an error. Real projects are different. Building a feature, migrating a database, setting up a monitoring stack – these take hours, span multiple sessions, involve parallel work streams, and must survive context window resets, session timeouts, and partial failures.

This article covers the architecture for workflows that last hours or days: how to model progress as a state machine, how to checkpoint for reliable resumption, how to delegate to parallel sub-agents without losing coherence, and how to recover when things fail partway through.

Progressive Agent Adoption: From First Task to Autonomous Workflows

Calendar February 22, 2026
Agent-Tooling
Beginner, Intermediate
Agent-Adoption-Planning, Trust-Building, Workflow-Design, Autonomy-Calibration
Adoption, Progressive-Autonomy, Getting-Started, Trust, Workflow-Evolution, Agent-Patterns, Onboarding, Human-Agent-Collaboration, Skill-Building
Claude-Code, Filesystem, Git

Progressive Agent Adoption#

Nobody goes from “I have never used an agent” to “my agent runs multi-hour autonomous workflows” in one step. Trust builds through experience. Each successful task at one level creates confidence to try the next. Skipping levels creates fear and bad outcomes — the agent does something unexpected, the human loses trust, and adoption stalls.

This article maps the adoption ladder from first task to autonomous workflows, with concrete examples of what to try at each level and signals that indicate readiness to move up.

Terraform Safety for Agents: Plans, Applies, and the Human Approval Gate

Calendar February 22, 2026
Infrastructure
Intermediate
Terraform-Plan-Interpretation, Risk-Assessment, Plan-Presentation, State-Lock-Management, Drift-Investigation
Terraform, Agent-Safety, Plan-Apply, Approval-Gates, State-Locks, Drift, Destructive-Actions, Risk-Assessment, Human-in-the-Loop
Terraform, Claude-Code

Terraform Safety for Agents#

Terraform is the most dangerous tool most agents have access to. A single terraform apply can create, modify, or destroy real infrastructure — databases with production data, networking that carries live traffic, security groups that protect running services. There is no undo button. terraform destroy is not an undo — it is a different destructive action.

This article defines the safety protocols agents must follow when working with Terraform: what to check before every plan, how to read plan output for danger, how to present plans to humans, when to apply vs when to stop, and how to handle state conflicts.

The ROI of Agent Infrastructure: Measuring Time Saved, Errors Avoided, and Projects Completed

Calendar February 22, 2026
Agent-Tooling
Beginner, Intermediate
Infrastructure-Roi-Analysis, Agent-Setup-Optimization, Productivity-Measurement
Roi, Agent-Infrastructure, Productivity, Time-Savings, Cost-Analysis, Investment, Checkpoints, Claude-Md, Skills, Memory, Context-Preservation, Adoption
Claude-Code, Filesystem, Git, Markdown

The ROI of Agent Infrastructure#

Most people skip agent infrastructure setup because the first task feels urgent. The second task is also urgent. By the tenth task, they have spent more time re-explaining context, correcting assumptions, and watching the agent re-derive decisions than the infrastructure would have cost to set up.

This article quantifies the return on agent infrastructure investment — not in abstract terms, but in minutes per session, tokens per project, and errors per workflow.