Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.
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Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.
[{"skill":"next-move","reason":"Decomposition and planning feed into agent execution"},{"skill":"windagszip","reason":"Compress skills that agents consume to save context budget"},{"skill":"task-decomposer","reason":"Breaks high-level tasks into agent-executable subtasks"},{"skill":"prompt-engineer","reason":"Optimize prompts that agents receive"}]
/agentic-patterns — Fundamentals of Effective Agent Behavior
You are teaching effective agentic patterns. These are model-agnostic principles — they work for Claude, GPT, Gemini, or any LLM acting as an agent. The goal: agents that decompose well, use tools precisely, recover from errors, manage their context budget, assess their own quality, and know when to stop.
When to Use
Use for:
Designing the control flow of a multi-step agent
Choosing between sequential, parallel, and hierarchical agent architectures
Implementing error recovery and graceful degradation
Managing context window budget across long agent runs
Building quality self-assessment into agent outputs
Deciding when an agent should halt vs continue
NOT for:
Building agent infrastructure or frameworks (use agent-creator)
Designing DAG topologies (use windags-architect)
Implementing specific tools (use the domain-specific skill)
Prompt optimization (use prompt-engineer)
The Five Pillars
Every effective agent embodies five capabilities:
1. DECOMPOSE — Break the problem into steps before acting
2. ORCHESTRATE — Choose and sequence tools with purpose
3. RECOVER — Handle failures without catastrophe
4. MANAGE — Spend context tokens like a budget
5. ASSESS — Know how well you did, and when to stop
Pillar 1: Decomposition
The Rule of Three Passes
Before acting, make three passes over the task:
Pass 1 — Scope: What does "done" look like? Define the exit condition first.
Pass 2 — Subtasks: What are the 3-7 concrete steps to reach "done"? Each step should be achievable with a single tool call or a small chain of tool calls.
Pass 3 — Dependencies: Which steps depend on which? Independent steps can parallelize. Dependent steps must serialize.
Decomposition Anti-Patterns
Anti-Pattern
Why It Fails
Fix
Acting before decomposing
Wasted tool calls, wrong direction
Always plan first, even briefly
One giant subtask
No parallelism, no checkpoints
Split until each subtask is one tool call
20+ subtasks
Cognitive overhead, lost context
Merge related steps, aim for 3-7
No exit condition
Agent runs forever
Define "done" before starting
Static plan
Can't adapt to discoveries
Replan after each wave of results
When to Re-Decompose
Replan when:
A tool call returns unexpected results
You discover the problem is different from what you assumed
A dependency fails and the downstream plan is invalid
You're 50% through and confidence in the remaining plan drops below 0.5
Pillar 2: Tool Orchestration
The Minimum Tool Principle
Use the fewest tools with the narrowest scope to accomplish each subtask. Every tool call has costs:
Tokens consumed (input + output)
Latency added
Error surface increased
Context budget spent
Wrong: Read 20 files to understand a codebase.
Right: Grep for the specific symbol, read the 2-3 files that contain it.
Tool Selection Heuristics
Need
Preferred Tool
Why
Find a file by name
Glob
Direct pattern match, no content scanning
Find content in files
Grep
Targeted search, returns locations
Understand a specific file
Read
Full context for one file
Understand a codebase
Task (explore agent)
Delegates exploration, protects context
Make a small change
Edit
Minimal diff, preserves surrounding code
Create something new
Write
Fresh file, no edit conflicts
Run a command
Bash
System interaction, build/test
Complex sub-problem
Task (subagent)
Isolates context, parallelizable
Sequential vs Parallel
Sequential (use when outputs feed into inputs):
Read file → understand structure → Edit specific section → Run tests
Parallel (use when tasks are independent):
[Grep for pattern A] + [Grep for pattern B] + [Read config file]
→ all complete → synthesize findings
Rule: If two tool calls don't share data, run them in parallel. If one needs the other's output, serialize them.
The Subagent Decision
Spawn a subagent (Task tool) when:
The sub-problem would consume >30% of your remaining context
The work is independent and can be described in one paragraph
You need to explore broadly (many files, web search) without polluting your context
The sub-problem maps to a known skill (code review, testing, research)
Do NOT spawn a subagent when:
The task is a single tool call
You need the result immediately for your next sentence
The overhead of describing the task exceeds the overhead of doing it
Pillar 3: Error Recovery
The Recovery Ladder
When a tool call fails, escalate through four levels:
Level 1 — Retry with adjustment: Fix the obvious issue (typo, wrong path, missing arg) and retry once.
Level 2 — Alternative approach: Use a different tool or strategy to achieve the same goal. If Edit fails, try a different Edit. If Grep finds nothing, try Glob with a different pattern.
Level 3 — Reduce scope: If the full task can't be completed, identify the largest subset that can. Deliver partial results with a clear note about what's missing.
Level 4 — Escalate to user: If you've tried levels 1-3 and the task is still blocked, describe what you tried, what failed, and ask the user for guidance. Never loop silently.
Error Recovery Anti-Patterns
Anti-Pattern
Consequence
Fix
Retry the same thing 5 times
Wasted tokens, same failure
One retry with adjustment, then Level 2
Ignore the error and continue
Cascading failures downstream
Every error must be handled
Simplify the task to make it work
User gets less than they asked for
Only reduce scope at Level 3, and disclose it
Give up immediately
User loses trust
Exhaust Level 1-2 before escalating
Structured Error Handling
When a tool call fails:
Read the error message carefully — it usually tells you what's wrong
Diagnose: Is it a transient issue (retry) or a fundamental problem (redesign)?
Act: Apply the appropriate recovery level
Report: If the error affects the final output, note it transparently
Pillar 4: Context Management
Context is a Budget
Every token in your context window costs money and attention. Treat context like a budget:
Income: User message, tool results, retrieved content
Spending: Each tool call adds to context
Savings: Subagents isolate expensive exploration
Debt: Unnecessary reads/searches that you can't un-read
The 30% Rule
Reserve 30% of your effective context for final synthesis and output. If you've used 70% of your context on research, stop researching and start synthesizing.
Context-Efficient Patterns
Pattern
How
Saves
Targeted reads
Read specific line ranges, not whole files
50-90% per file
Grep before read
Find the exact location, then read only that section
Avoids reading irrelevant files
Subagent delegation
Expensive exploration happens in isolated context
Protects main context
Summarize early
After a research phase, write a summary before continuing
Prevents re-reading
Batch tool calls
Run independent calls in parallel
Reduces round trips
What NOT to Load Into Context
Entire files when you need 10 lines
Build output or test logs beyond the relevant failure
Files you've already read and understood
Exploratory searches when you already know the answer
Pillar 5: Quality Self-Assessment
Confidence Calibration
After completing a task, assess your confidence on two axes:
Completeness: Did you address everything the user asked for?
1.0: Every aspect addressed with evidence
0.7: Main request addressed, some secondary aspects missing
0.4: Partial answer, significant gaps
0.1: Barely started
Correctness: How likely is your output to be right?
1.0: Verified by tests, cross-referenced, high certainty
0.7: Reasonable confidence but not verified
0.4: Best guess, significant uncertainty
0.1: Speculative
When to Stop
Stop when ANY of these are true:
The exit condition (from decomposition) is satisfied
Your confidence that further work improves the output drops below 0.3
You've consumed 70% of available context (the 30% rule)
The user's question has been answered completely
You're making changes that don't measurably improve the result
Continue when ALL of these are true:
The exit condition is not yet met
You have a clear next step with expected improvement
You have sufficient context budget remaining
Each iteration is producing measurable progress
The "One More Thing" Trap
Resist the urge to add improvements the user didn't ask for. Every "one more thing" costs tokens, risks introducing bugs, and delays delivery. If you see an improvement opportunity, note it in your response — don't implement it unasked.
Architecture Patterns
Pattern 1: Scout-Then-Act
Phase 1 (Scout): Read, Grep, Glob — understand the territory
Phase 2 (Plan): Decompose based on what you found
Phase 3 (Act): Edit, Write, Bash — execute the plan
Phase 4 (Verify): Run tests, check results
Best for: Bug fixes, feature additions, refactoring. You need to understand before you change.
Pattern 2: Parallel Fan-Out
Wave 0: [Research A] + [Research B] + [Research C] ← parallel subagents
Wave 1: [Synthesize findings] ← single agent
Wave 2: [Implement based on synthesis] ← single agent
Best for: Tasks requiring multiple independent information sources. Research tasks, competitive analysis, multi-file understanding.
Pattern 3: Iterative Refinement
Loop:
1. Produce draft output
2. Evaluate against criteria
3. If criteria met → done
4. Identify largest gap
5. Fix the gap → go to 1
Max iterations: 3-5
Best for: Creative tasks, code generation, content production. Each pass improves quality.
Pattern 4: Staged Pipeline
Stage 1: Raw extraction (fast, broad)
Stage 2: Filtering (remove noise)
Stage 3: Enrichment (add detail to survivors)
Stage 4: Final synthesis
Best for: Data processing, research synthesis, skill compression. Each stage narrows the working set.
Quality Checklist
Before considering an agentic task complete:
[ ] Exit condition defined before starting
[ ] Task decomposed into 3-7 concrete subtasks
[ ] Dependencies identified (what must serialize vs parallelize)
[ ] Each tool call has a clear purpose (no exploratory fishing)
[ ] Errors handled at the appropriate recovery level
[ ] Context budget tracked (not over 70% before synthesis)
[ ] Output addresses every part of the user's request
[ ] Confidence self-assessed on completeness and correctness
[ ] Improvements not requested by user noted but not implemented
[ ] Clear stopping point reached (exit condition met)
The Meta-Pattern
All five pillars follow one meta-pattern: think before acting, act with precision, assess after acting.
THINK: What am I trying to do? How will I know it's done?
ACT: Use the minimum tools with maximum precision.
ASSESS: Did it work? What's my confidence? Should I continue?
Agents that skip THINK waste tokens exploring. Agents that skip ASSESS don't know when to stop. Agents that skip ACT just plan forever. All three, in that order, every cycle.
