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What are Claude Code dynamic workflows? A practitioner's breakdown of six orchestration patterns, three single-window failure modes, and a pattern selection decision table — so you know when to use them, how to use them, and how to control costs.
Claude Code dynamic workflows let you stop doing everything in a single conversation window. Instead of one Claude grinding through a massive task start to finish, dynamic workflows automatically decompose your objective into dozens — sometimes hundreds — of independent subtasks, dispatch separate Claude instances to execute them in parallel, cross-verify results, and return only validated conclusions. Anthropic shipped this capability on May 28, 2026.
This guide covers the three structural failures that make single-window agents unreliable at scale, the six orchestration patterns Anthropic defined, a decision table for picking the right pattern, and practical ultracode cost controls based on real usage.
Key takeaways
Dynamic workflows add an orchestration layer on top of Claude Code conversations. When you describe a task, Claude no longer asks "what next?" turn by turn. It generates a decomposition plan, dispatches independent subagents to execute subtasks in parallel, and synthesizes results — all while your conversation window stays responsive.
Three components make this work:
The distinction from regular subagents is not about parallelism. It is about who holds the plan. A subagent's plan lives inside the conversation context and degrades as the conversation grows. A dynamic workflow's plan lives in a script — it never distorts regardless of conversation length.
Dynamic workflows do not make Claude smarter. They solve three structural failures that emerge when a single-window agent handles complex, long-running tasks. No amount of prompt engineering eliminates these.
Agentic laziness occurs when Claude completes part of a task and declares "done" prematurely. Ask it to audit 200 files for security vulnerabilities. It carefully examines the first 30, then reports "audit complete, 5 issues found." The remaining 170 files never get checked.
This is not laziness in the human sense. As intermediate results accumulate in the context window, Claude's boundary between "finished" and "still pending" blurs.
Dynamic workflows fix this with loop-driven execution: the script defines explicit stop conditions (e.g., "all 200 files scanned with zero new findings"). Until the condition is met, the workflow keeps dispatching agents. No early exits.
Self-preferential bias means Claude tends to approve its own output. Ask it to write code and then review that code — it will almost certainly say "looks good." The same brain that produced the work is judging it. Blind spots stay blind.
Dynamic workflows fix this with role separation: the producer and the verifier are different subagents with isolated context windows. The verifier never sees the producer's reasoning process — only the output. This is an architectural solution, not a prompt trick.
In my own knowledge-base project, I ran the same audit task two ways. Single-window self-review caught 3 issues. Splitting production and verification into separate subagents surfaced 11. Same task, same Claude model — different architecture, dramatically different results.
Goal drift happens in long conversations. Your original request had 20 specific requirements. After 30 turns, context-window compression has dropped the early details. Claude remembers only the last few turns and silently deviates from the original objective.
Dynamic workflows fix this with plan pinning: requirements are locked into script variables. Every subagent receives the complete, undistorted instructions regardless of how long the parent conversation has run.
These three failures are not Claude-specific bugs. They are inherent to any large language model operating in long-context conversations. Dynamic workflows trade some conversational flexibility for structural reliability — a worthwhile exchange when tasks demand precision.
"Dynamic" is relative to static orchestration. Static orchestration means you pre-build a fixed pipeline (using tools like n8n or Make) that runs the same steps every time. Dynamic workflows let Claude generate the orchestration logic on the fly based on your objective.
| Dimension | Dynamic Workflows | Static Orchestration |
|---|---|---|
| Orchestration logic | Claude generates per-run; may vary | You pre-define; identical every run |
| Best for | Uncertain task structure; needs Claude's judgment | Proven, repeatable processes |
| Token cost | Higher (plan generation + parallel subtasks) | Predictable, usually lower |
| Reusability | Saveable as commands; may adjust per run | Deterministic repeat execution |
| Debugging | Moderate (auto-generated but readable scripts) | Low (you wrote it, you understand it) |
| Crash recovery | Built-in checkpoint resume | Depends on your implementation |
The decision rule is straightforward:
Not every task benefits from a workflow. If a single conversation turn solves the problem, adding a workflow only increases latency and cost. The litmus test: does this task need multiple independent perspectives or parallel processing of many subtasks? If neither, skip the workflow.
Anthropic defined six composable orchestration patterns in their official post "A Harness for Every Task." Each pattern solves a different class of problem. Picking the wrong pattern is worse than picking none.
The simplest pattern. A classifier agent reads the task, determines its type, and routes it to the appropriate specialist agent.
Use this for customer tickets (technical vs. billing vs. feature request), bug triage (frontend vs. backend vs. database), or any scenario where different categories need different handling.
The classifier's accuracy is everything. If classification fails, all downstream work is wasted. In practice, exhaustively enumerate every possible category in your prompt with one or two examples per category.
The most widely used pattern. Decompose a large task into independent subtasks, assign each to a separate agent, execute all in parallel, and merge results when every agent finishes.
Audit 200 files for security vulnerabilities — each file gets its own agent, 200 agents run simultaneously, results are consolidated. A serial audit taking hours completes in the time of the slowest single file plus merge overhead.
Critical constraint: subtasks must be fully independent. If file A's audit depends on file B's structure, you cannot use pure fan-out — run a dependency analysis first, then group accordingly.
The most distinctive pattern. For every producer agent, a dedicated verifier agent attempts to disprove the producer's conclusions against predefined criteria. Only claims the verifier cannot refute enter the final report.
This directly neutralizes self-preferential bias. Producer and verifier have isolated contexts — the verifier sees only the output, never the reasoning. Every deliverable gets a dedicated adversary.
I use this pattern extensively for research validation. When a subagent claims "tool X outperforms tool Y by 3x," a separate verifier agent searches for contradicting benchmarks, checks the methodology, and flags unsubstantiated claims. The result: research reports I actually trust.
Batch-generate candidate solutions, then filter using objective, measurable criteria — test pass rates, performance benchmarks, code line counts. Deduplicate and keep the top candidates.
Generate 10 implementations of a function, run benchmarks, keep the fastest. Generate 20 headline candidates, score by predicted click-through rate, keep the top 3.
This differs from the tournament pattern: generate-and-filter uses quantifiable metrics (pass rate, latency, code size), while tournaments use subjective comparison (readability, design aesthetics, user experience).
N agents solve the same problem using different approaches. A judge agent compares solutions pairwise, eliminating losers round by round until one winner remains.
Pairwise comparison ("Is A better than B, and why?") produces more reliable rankings than absolute scoring ("Rate A on a scale of 1-10"). This finding has been validated repeatedly in LLM evaluation research. The tournament pattern builds this insight into the orchestration architecture.
Use tournaments for refactoring approach selection, copy A/B testing, or design review — any scenario requiring subjective quality judgment.
An open-ended discovery pattern. Keep dispatching agents to explore until a stopping condition triggers: two consecutive rounds with zero new findings, remaining errors at zero, coverage target reached.
Fix compilation errors in a codebase — fixing one may expose two more. Keep fixing until the build is clean. Open-ended search — keep expanding search angles until nothing new surfaces.
The risk is runaway execution. A poorly defined stopping condition lets the loop burn tokens indefinitely. Always set at least two safety rails: one business condition ("two consecutive rounds with zero new findings") plus one hard cap ("maximum 10 iterations").
Real workflows rarely use a single pattern. The Bun rewrite combined classify-and-act (lifecycle mapping), fan-out-and-synthesize with adversarial verification (parallel migration), and loop-until-done (fix iteration). Pattern selection is not multiple choice — it is combination.
| Task Characteristic | Recommended Pattern | Watch Out For |
|---|---|---|
| Tasks split cleanly into distinct categories | Classify-and-Act | Classifier accuracy determines all downstream quality |
| Many structurally identical subtasks, parallelizable | Fan-Out-and-Synthesize | Subtasks must be fully independent — no cross-dependencies |
| Output demands strict correctness guarantees | Adversarial Verification | Roughly doubles token consumption |
| Need to explore multiple candidate solutions | Generate-and-Filter | Must have objectively measurable selection criteria |
| Quality judgment is inherently subjective | Tournament | Define judging criteria explicitly in the prompt |
| Unknown number of iterations to completion | Loop-Until-Done | Design stopping conditions carefully to prevent runaway |
| Task combines decomposition, verification, and iteration | Multi-pattern combination | Identify the backbone pattern first, layer others at key nodes |
| Solvable in a single conversation turn | No workflow needed | Adding a workflow only increases cost and latency |
A quick decision formula: task requires 3+ independent parallel subtasks, OR output needs independent verification — use a workflow. Otherwise, solve it directly in conversation.
Before starting any task, ask three questions: Can this task decompose into independent subtasks? Does the output need cross-verification or multi-perspective comparison? Will it take more than 15 conversation turns? Two "yes" answers justify a workflow.
These prompts are ready to paste into Claude Code. They perform best under ultracode mode:
Deep Research
Use a workflow to do deep research on "{your topic}." Gather information from at least 5 independent sources, cross-verify every key claim, and produce a report with source citations.
Full-Codebase Security Audit
Use a workflow to scan the entire project for security vulnerabilities. Check each file independently, adversarially verify every finding, and include only confirmed issues in the report.
Business Plan Stress Test
Read my business plan, then use a workflow to critique it from three perspectives: investor, customer, and competitor. Each perspective analyzes independently. Produce a consolidated list of core challenges from all three.
Resume Screening
This folder contains 50 resumes. Use a workflow to rank them by these criteria: {your criteria}. Do an initial screen, cross-verify the Top 10, and produce a final ranking with reasoning.
CLAUDE.md Rule Mining
Use a workflow to review my last 30 conversations, find patterns where I repeatedly corrected Claude, categorize those corrections, and convert them into CLAUDE.md rules.
These prompts share a principle: describe the objective and constraints, not the implementation. Claude decides which orchestration pattern to use, how many subagents to dispatch, and how to verify results. You control "what" and "to what standard" — not "how."
Dynamic workflows handle far more than code. The following ten scenarios draw from Anthropic's official documentation and early adopter feedback (source: Anthropic blog).
Deep Research — Gather, cross-verify, and synthesize information scattered across dozens of sources. Claude Code's built-in /deep-research command implements this pattern. Use fan-out-and-synthesize + adversarial verification + loop-until-done.
Fact-Checking — Verify every factual claim in a report or article. Each claim gets a dedicated agent. Unverifiable claims are flagged as uncertain. Use fan-out-and-synthesize + adversarial verification.
Batch Classification — Process 200 tickets, alerts, or review requests. Classify first, then route by type to specialized handlers. Use classify-and-act + fan-out-and-synthesize.
Solution Exploration — Generate multiple UX designs, implementation approaches, or naming options, then select the best through objective metrics or pairwise comparison. Use generate-and-filter or tournament.
Root Cause Analysis — Investigate a complex bug from multiple angles simultaneously — logs, code change history, performance metrics — then cross-reference findings. Use fan-out-and-synthesize + adversarial verification.
Rule Compliance Scanning — Check whether a codebase follows a specific ruleset (security policy, coding standards, compliance requirements). Each rule gets a dedicated agent scanning the entire repository. Use fan-out-and-synthesize.
Evaluation and Optimization — Run a full quality audit on a system, fix discovered issues immediately, re-verify, and loop until all checks pass. Use fan-out-and-synthesize + loop-until-done.
Large-Scale Migration — Migrate an entire codebase from one framework to another. Each module gets a dedicated agent for independent migration, with verification agents confirming behavioral consistency. Use fan-out-and-synthesize + adversarial verification.
Model Routing — Not every subtask in a workflow needs the strongest model. Route simple classification to a fast model and complex reasoning to a powerful one, balancing quality and cost dynamically. Use classify-and-act (routed by complexity).
Tournament Ranking — Compare 10 refactoring proposals, copy variants, or design concepts through pairwise elimination to find the winner. Use tournament.
Ultracode is the top tier of dynamic workflows. It does two things: maximizes Claude's thinking depth and enables automatic workflow orchestration — Claude decides autonomously whether each task warrants a workflow.
Ordered by frequency of use:
/effort ultracode in Claude Code. The current session enters ultracode mode./deep-research <your question> to trigger the predefined deep-research workflow.Ultracode applies to the current session only. New sessions reset automatically. Switch back to daily work with /effort high.
First-time recommendation: Start with /deep-research "a technical topic you're curious about" to see automatic orchestration in action. Then try /effort ultracode on a small project with a natural-language instruction (e.g., "audit error handling in the src/ directory"). Graduate to specifying patterns explicitly (e.g., "use fan-out-and-synthesize to audit in parallel") once you understand the mechanics.
No built-in spending cap exists. A complex task under ultracode can burn tokens far beyond expectations. Practical cost controls:
/workflows view to watch per-agent token consumption in real time. Stop immediately if things go sideways./model before large runs. Consider routing lightweight subtasks to cheaper models./effort high. Only activate ultracode for multi-agent deep orchestration on tasks that justify the cost./cost to record actual token usage and duration. Use this as a budget reference for similar future tasks.Dynamic workflows enforce built-in safety constraints: a maximum of 1,000 subagents per run, with a concurrency ceiling of 16 (reduced on machines with fewer CPU cores). You cannot inject additional instructions during a run — you can only pause for permission confirmations. These limits prevent runaway execution (source: Claude Code docs).
Ultracode does not automatically mean better. If your task needs neither parallel processing nor cross-verification, ultracode only increases cost without improving quality. Decision rule: if
/effort highalready produces satisfactory results, stay there.
When a workflow produces good results, you do not need to re-trigger it from scratch every time.
Save: Run /workflows, select the target run, press s. Two save locations:
.claude/workflows/ — project-level. Anyone who clones the repository gets the workflow.~/.claude/workflows/ — personal-level. Available across all your projects.Saved workflows appear as /<name> in slash-command autocomplete.
Share: Save to .claude/workflows/ and commit to Git. Team members who clone the repository automatically have the workflow. Zero extra configuration.
Combine with /loop and /goal: Use /goal to define acceptance criteria ("all tests pass"), activate ultracode for workflow orchestration, and use /loop to keep Claude iterating until the goal is met. This combination excels at large-scale refactoring — humans define objectives and boundaries, Claude figures out how to get there.
You want to understand "which AI coding tools are worth watching in 2026." Without dynamic workflows, the process looks like: search manually, open links one by one, read articles, take notes, cross-verify, compile a report — at least an hour or two.
With dynamic workflows, you say one sentence:
Use a workflow to do deep research on "AI coding tools worth watching in 2026." Gather from at least 8 independent sources, cross-verify every key claim, and produce a report with source citations.
Claude automatically decomposes the task. Several subagents search different channels in parallel. Others verify whether the first batch's findings are reliable. A final agent compiles everything into a cited, cross-verified report. Total elapsed time: roughly 10 minutes.
The core value is not speed — it is that you describe what you want, and Claude decides how to organize the team to deliver it.
How far can dynamic workflows scale? Jarred Sumner, creator of Bun (a JavaScript runtime), used dynamic workflows to migrate Bun's core engine from one programming language to another — producing approximately 750,000 lines of code from start to merge in 11 days (source: Anthropic blog; the project has not yet shipped to production).
The significance is not the number itself. It validates a new working model: humans define objectives and boundaries, AI orchestrates parallel execution, humans perform final review. Jarred reviewed the previous night's workflow output during the day and launched the next batch at night.
Important caveat: Jarred has deep compiler development expertise. This pace is not representative of average users. Treat it as a reference for the upper bound of what dynamic workflows can accomplish at scale.
What is the difference between dynamic workflows and subagents?
A subagent is an independent Claude instance dispatched for a specific task — Claude decides turn by turn whether to spawn one. A dynamic workflow is a JavaScript orchestration script running in a separate runtime, capable of scheduling dozens to thousands of subagents in parallel. The difference: a subagent's plan lives in the conversation context (and degrades over time); a workflow's plan lives in the script (and stays intact).
Can I use dynamic workflows on the Pro plan?
Yes. Pro plan has it disabled by default — enable it in /config under Dynamic workflows. Max and Team plans enable it by default. Enterprise plans require admin activation in the Claude Code admin dashboard. Requires Claude Code v2.1.154 or higher.
How many tokens does ultracode consume?
Depends on task complexity and agent count. Community feedback reports runs exceeding 1.7 million tokens in minutes (source: Reddit r/ClaudeAI). Start small, set explicit token budgets, and monitor with /workflows.
What happens if a workflow is interrupted?
Dynamic workflows support crash recovery. Progress persists to disk, and the next launch resumes from the checkpoint. Completed subagent results are never lost (source: Claude Code docs).
How many subagents can a single workflow run?
Maximum 1,000 subagents per run, with a concurrency ceiling of 16 (reduced on machines with fewer CPU cores). The runtime enforces these limits to prevent runaway scripts.
Can dynamic workflows handle non-code tasks?
Yes. Deep research, fact-checking, investment analysis, resume screening, and business plan stress testing are all proven non-code scenarios. The criterion: can the task decompose into parallelizable subtasks, and does the output need independent verification?
Can I combine multiple orchestration patterns?
Yes, and real workflows almost always do. The Bun rewrite used classify-and-act for lifecycle mapping, fan-out-and-synthesize with adversarial verification for parallel migration, and loop-until-done for fix iteration. Pattern selection is combinatorial, not exclusive.
How do I save a workflow for reuse?
Run /workflows, select the target run, press s. Save to .claude/workflows/ (project-level, shared via Git) or ~/.claude/workflows/ (personal, cross-project). Saved workflows appear as slash commands in autocomplete.
How do I disable dynamic workflows?
Disable in /config under Dynamic workflows. Alternatively, set "disableWorkflows": true in ~/.claude/settings.json, or use the environment variable CLAUDE_CODE_DISABLE_WORKFLOWS=1. Organization-level disabling uses managed settings or the admin dashboard.
Do API and cloud deployments (Bedrock / Vertex AI) support dynamic workflows?
Yes. Dynamic workflows are enabled by default on API, Amazon Bedrock, Google Vertex AI, and Foundry.
Dynamic workflows changed how I approach complex tasks with Claude Code. The improvement is not about intelligence — it is about collaboration structure. In my own projects, single-window runs start losing details around turn 20. Splitting the same task into a workflow with 50 independent subagents — each responsible for its own scope, cross-checking each other — produces visibly better output.
But it is not a universal solution. Simple tasks do not benefit. Cost-sensitive scenarios require careful token management. And extreme cases like the Bun migration still lack full production validation.
If you already use Claude Code, start with /deep-research to see orchestration in action. Then try ultracode on one of your own research or audit tasks. Copy the prompt examples from this guide, and experience the full cycle — describe an objective, watch Claude auto-orchestrate, see multiple agents work in parallel, and receive a cross-verified synthesis. That hands-on experience teaches more than reading ever could.
— Leo
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