Autonomous Agents V2

Autonomous Agents

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/autonomous-agents from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Autonomous Agents Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Scope, Tooling, Patterns, Basic ReAct Implementation, LangGraph ReAct (Production).

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• User mentions or implies: autonomous agent
• User mentions or implies: autogpt
• User mentions or implies: babyagi
• User mentions or implies: self-prompting
• User mentions or implies: goal decomposition
• User mentions or implies: react pattern

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Combine steps where possible # Prefer fewer, more capable steps over many small ones # Use structured outputs (JSON schemas) # Add validation at each step # Use better models for critical steps

2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: Reduce step count

Combine steps where possible

Prefer fewer, more capable steps over many small ones

Imported: Increase per-step reliability

Use structured outputs (JSON schemas)

Add validation at each step

Use better models for critical steps

Imported: Capabilities

• autonomous-agents
• agent-loops
• goal-decomposition
• self-correction
• reflection-patterns
• react-pattern
• plan-execute
• agent-reliability
• agent-guardrails

Examples

Example 1: Ask for the upstream workflow directly

Use @autonomous-agents-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @autonomous-agents-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @autonomous-agents-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @autonomous-agents-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Track context usage

class ContextManager: def init(self, max_tokens=100000): self.max_tokens = max_tokens self.messages = []

def add(self, message):
    self.messages.append(message)
    self.maybe_compact()

def maybe_compact(self):
    if self.token_count() > self.max_tokens * 0.8:
        self.compact()

def compact(self):
    # Always keep: system prompt
    system = self.messages[0]

    # Always keep: last N messages
    recent = self.messages[-10:]

    # Summarize: everything else
    middle = self.messages[1:-10]
    if middle:
        summary = summarize_messages(middle)
        self.messages = [system, summary] + recent

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Reliability over autonomy - every step compounds error probability
• Constrain scope - domain-specific beats general-purpose
• Treat outputs as proposals, not truth
• Build guardrails before expanding capabilities
• Human-in-the-loop for critical decisions is non-negotiable
• Log everything - every action must be auditable
• Fail safely with rollback, not silently with corruption

Imported Operating Notes

Imported: Principles

• Reliability over autonomy - every step compounds error probability
• Constrain scope - domain-specific beats general-purpose
• Treat outputs as proposals, not truth
• Build guardrails before expanding capabilities
• Human-in-the-loop for critical decisions is non-negotiable
• Log everything - every action must be auditable
• Fail safely with rollback, not silently with corruption

Imported: Multi-Layer Guardrails

""" class GuardedAgent: def init(self, agent, config): self.agent = agent self.max_cost = config.get("max_cost_usd", 1.0) self.max_steps = config.get("max_steps", 10) self.allowed_actions = config.get("allowed_actions", []) self.require_approval = config.get("require_approval", [])

async def execute(self, goal):
    total_cost = 0
    steps = 0

    while steps < self.max_steps:
        # Get next action
        action = await self.agent.plan_next(goal)

        # Validate action is allowed
        if action.name not in self.allowed_actions:
            raise ActionNotAllowedError(action.name)

        # Check if approval needed
        if action.name in self.require_approval:
            approved = await self.request_human_approval(action)
            if not approved:
                return {"status": "rejected", "action": action}

        # Estimate cost
        estimated_cost = self.estimate_cost(action)
        if total_cost + estimated_cost > self.max_cost:
            raise CostLimitExceededError(total_cost)

        # Execute with rollback capability
        checkpoint = await self.save_checkpoint()
        try:
            result = await self.agent.execute(action)
            total_cost += self.actual_cost(action)
            steps += 1
        except Exception as e:
            await self.rollback_to(checkpoint)
            raise

        if result.is_complete:
            break

    return {"status": "complete", "total_cost": total_cost}

"""

Imported: Least Privilege Principle

"""

Define minimal permissions per task type

TASK_PERMISSIONS = { "research": ["web_search", "read_file"], "coding": ["read_file", "write_file", "run_tests"], "admin": ["all"], # Rarely grant this }

def create_scoped_agent(task_type): allowed = TASK_PERMISSIONS.get(task_type, []) tools = [t for t in ALL_TOOLS if t.name in allowed] return Agent(tools=tools) """

Imported: Least privilege principle

PERMISSIONS = { "research_agent": ["read_web", "read_docs"], "code_agent": ["read_file", "write_file", "run_tests"], "email_agent": ["read_email", "draft_email"], # NOT send "admin_agent": ["all"], # Rarely used }

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/autonomous-agents, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Design for failure

class RobustAgent: def execute_with_retry(self, step, max_retries=3): for attempt in range(max_retries): try: result = step.execute() if self.validate(result): return result except Exception as e: if attempt == max_retries - 1: raise self.log_retry(step, attempt, e)

Imported: Design for partial failure

Each step is independently recoverable

Checkpoint before external calls

Rollback capability for each integration

Agent Takes Dangerous Actions

Severity: HIGH

Situation: Agent with broad permissions

Symptoms: Agent deletes production data. Sends emails to wrong recipients. Makes purchases without approval. Modifies settings it shouldn't. Actions that can't be undone.

Why this breaks: Agents optimize for their goal. Without guardrails, they'll take the shortest path - even if that path is destructive. An agent told to "clean up the database" might interpret that as "delete everything."

Broad permissions + autonomy + goal optimization = danger.

Recommended fix:

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Scope

• multi-agent-systems → multi-agent-orchestration
• tool-building → agent-tool-builder
• memory-systems → agent-memory-systems
• workflow-orchestration → workflow-automation

Imported: Tooling

Frameworks

• LangGraph - When: Production agents with state management Note: 1.0 released Oct 2025, checkpointing, human-in-loop
• AutoGPT - When: Research/experimentation, open-ended exploration Note: Needs external guardrails for production
• CrewAI - When: Role-based agent teams Note: Good for specialized agent collaboration
• Claude Agent SDK - When: Anthropic ecosystem agents Note: Computer use, tool execution

Patterns

• ReAct - When: Reasoning + Acting in alternating steps Note: Foundation for most modern agents
• Plan-Execute - When: Separate planning from execution Note: Better for complex multi-step tasks
• Reflection - When: Self-evaluation and correction Note: Evaluator-optimizer loop

Imported: Patterns

ReAct Agent Loop

Alternating reasoning and action steps

When to use: Interactive problem-solving, tool use, exploration

REACT PATTERN:

""" The ReAct loop:

1. Thought: Reason about what to do next
2. Action: Choose and execute a tool
3. Observation: Receive result
4. Repeat until goal achieved

Key: Explicit reasoning traces make debugging possible """

Imported: Basic ReAct Implementation

""" from langchain.agents import create_react_agent from langchain_openai import ChatOpenAI

Define the ReAct prompt template

react_prompt = ''' Answer the question using the following format:

Question: the input question Thought: reason about what to do Action: tool_name Action Input: input to the tool Observation: result of the action ... (repeat Thought/Action/Observation as needed) Thought: I now know the final answer Final Answer: the answer '''

Create the agent

agent = create_react_agent( llm=ChatOpenAI(model="gpt-4o"), tools=tools, prompt=react_prompt, )

Execute with step limit

result = agent.invoke( {"input": query}, config={"max_iterations": 10} # Prevent runaway loops ) """

Imported: LangGraph ReAct (Production)

""" from langgraph.prebuilt import create_react_agent from langgraph.checkpoint.postgres import PostgresSaver

Production checkpointer

checkpointer = PostgresSaver.from_conn_string( os.environ["POSTGRES_URL"] )

agent = create_react_agent( model=llm, tools=tools, checkpointer=checkpointer, # Durable state )

Invoke with thread for state persistence

config = {"configurable": {"thread_id": "user-123"}} result = agent.invoke({"messages": [query]}, config) """

Plan-Execute Pattern

Separate planning phase from execution

When to use: Complex multi-step tasks, when full plan visibility matters

PLAN-EXECUTE PATTERN:

""" Two-phase approach:

1. Planning: Decompose goal into subtasks
2. Execution: Execute subtasks, potentially re-plan

Advantages:

• Full visibility into plan before execution
• Can validate/modify plan with human
• Cleaner separation of concerns

Disadvantages:

• Less adaptive to mid-task discoveries
• Plan may become stale """

Imported: LangGraph Plan-Execute

""" from langgraph.prebuilt import create_plan_and_execute_agent

Planner creates the task list

planner_prompt = ''' For the given objective, create a step-by-step plan. Each step should be atomic and actionable. Format: numbered list of steps. '''

Executor handles individual steps

executor_prompt = ''' You are executing step {step_number} of the plan. Previous results: {previous_results} Current step: {current_step} Execute this step using available tools. '''

agent = create_plan_and_execute_agent( planner=planner_llm, executor=executor_llm, tools=tools, replan_on_error=True, # Re-plan if step fails )

Human approval of plan

config = { "configurable": { "thread_id": "task-456", }, "interrupt_before": ["execute"], # Pause before execution }

First call creates plan

plan = agent.invoke({"objective": goal}, config)

Review plan, then continue

if human_approves(plan): result = agent.invoke(None, config) # Continue from checkpoint """

Imported: Decomposition Strategies

"""

Decomposition-First: Plan everything, then execute

Best for: Stable tasks, need full plan approval

Interleaved: Plan one step, execute, repeat

Best for: Dynamic tasks, learning as you go

def interleaved_execute(goal, max_steps=10): state = {"goal": goal, "completed": [], "remaining": [goal]}

for step in range(max_steps):
    # Plan next action based on current state
    next_action = planner.plan_next(state)

    if next_action == "DONE":
        break

    # Execute and update state
    result = executor.execute(next_action)
    state["completed"].append((next_action, result))

    # Re-evaluate remaining work
    state["remaining"] = planner.reassess(state)

return state

"""

Reflection Pattern

Self-evaluation and iterative improvement

When to use: Quality matters, complex outputs, creative tasks

REFLECTION PATTERN:

""" Self-correction loop:

1. Generate initial output
2. Evaluate against criteria
3. Critique and identify issues
4. Refine based on critique
5. Repeat until satisfactory

Also called: Evaluator-Optimizer, Self-Critique """

Imported: Basic Reflection

""" def reflect_and_improve(task, max_iterations=3): # Initial generation output = generator.generate(task)

for i in range(max_iterations):
    # Evaluate output
    critique = evaluator.critique(
        task=task,
        output=output,
        criteria=[
            "Correctness",
            "Completeness",
            "Clarity",
        ]
    )

    if critique["passes_all"]:
        return output

    # Refine based on critique
    output = generator.refine(
        task=task,
        previous_output=output,
        critique=critique["feedback"],
    )

return output  # Best effort after max iterations

"""

Imported: LangGraph Reflection

""" from langgraph.graph import StateGraph

def build_reflection_graph(): graph = StateGraph(ReflectionState)

# Nodes
graph.add_node("generate", generate_node)
graph.add_node("reflect", reflect_node)
graph.add_node("output", output_node)

# Edges
graph.add_edge("generate", "reflect")
graph.add_conditional_edges(
    "reflect",
    should_continue,
    {
        "continue": "generate",  # Loop back
        "end": "output",
    }
)

return graph.compile()

def should_continue(state): if state["iteration"] >= 3: return "end" if state["score"] >= 0.9: return "end" return "continue" """

Imported: Separate Evaluator (More Robust)

"""

Use different model for evaluation to avoid self-bias

generator = ChatOpenAI(model="gpt-4o") evaluator = ChatOpenAI(model="gpt-4o-mini") # Different perspective

Or use specialized evaluators

from langchain.evaluation import load_evaluator evaluator = load_evaluator("criteria", criteria="correctness") """

Guardrailed Autonomy

Constrained agents with safety boundaries

When to use: Production systems, critical operations

GUARDRAILED AUTONOMY:

""" Production agents need multiple safety layers:

1. Input validation
2. Action constraints
3. Output validation
4. Cost limits
5. Human escalation
6. Rollback capability """

Imported: Cost Control

"""

Context length grows quadratically in cost

Double context = 4x cost

def trim_context(messages, max_tokens=4000): # Keep system message and recent messages system = messages[0] recent = messages[-10:]

# Summarize middle if needed
if len(messages) > 11:
    middle = messages[1:-10]
    summary = summarize(middle)
    return [system, summary] + recent

return messages

"""

Durable Execution Pattern

Agents that survive failures and resume

When to use: Long-running tasks, production systems, multi-day processes

DURABLE EXECUTION:

""" Production agents must:

• Survive server restarts
• Resume from exact point of failure
• Handle hours/days of runtime
• Allow human intervention mid-process

LangGraph 1.0 provides this natively. """

Imported: LangGraph Checkpointing

""" from langgraph.checkpoint.postgres import PostgresSaver from langgraph.graph import StateGraph

Production checkpointer (not MemorySaver!)

checkpointer = PostgresSaver.from_conn_string( os.environ["POSTGRES_URL"] )

Build graph with checkpointing

graph = StateGraph(AgentState)

... add nodes and edges ...

agent = graph.compile(checkpointer=checkpointer)

Each invocation saves state

config = {"configurable": {"thread_id": "long-task-789"}}

Start task

agent.invoke({"goal": complex_goal}, config)

If server dies, resume later:

state = agent.get_state(config) if not state.is_complete: agent.invoke(None, config) # Continues from checkpoint """

Imported: Human-in-the-Loop Interrupts

"""

Pause at specific nodes

agent = graph.compile( checkpointer=checkpointer, interrupt_before=["critical_action"], # Pause before interrupt_after=["validation"], # Pause after )

First invocation pauses at interrupt

result = agent.invoke({"goal": goal}, config)

Human reviews state

state = agent.get_state(config) if human_approves(state): # Continue from pause point agent.invoke(None, config) else: # Modify state and continue agent.update_state(config, {"approved": False}) agent.invoke(None, config) """

Imported: Time-Travel Debugging

"""

LangGraph stores full history

history = list(agent.get_state_history(config))

Go back to any previous state

past_state = history[5] agent.update_state(config, past_state.values)

Replay from that point with modifications

agent.invoke(None, config) """

Imported: Sharp Edges

Error Probability Compounds Exponentially

Severity: CRITICAL

Situation: Building multi-step autonomous agents

Symptoms: Agent works in demos but fails in production. Simple tasks succeed, complex tasks fail mysteriously. Success rate drops dramatically as task complexity increases. Users lose trust.

Why this breaks: Each step has independent failure probability. A 95% success rate per step sounds great until you realize:

• 5 steps: 77% success (0.95^5)
• 10 steps: 60% success (0.95^10)
• 20 steps: 36% success (0.95^20)

This is the fundamental limit of autonomous agents. Every additional step multiplies failure probability.

Recommended fix:

Imported: Break into checkpointed segments

Human review at each segment

Resume from last good checkpoint

API Costs Explode with Context Growth

Severity: CRITICAL

Situation: Running agents with growing conversation context

Symptoms: $47 to close a single support ticket. Thousands in surprise API bills. Agents getting slower as they run longer. Token counts exceeding model limits.

Why this breaks: Transformer costs scale quadratically with context length. Double the context, quadruple the compute. A long-running agent that re-sends its full conversation each turn can burn money exponentially.

Most agents append to context without trimming. Context grows:

• Turn 1: 500 tokens → $0.01
• Turn 10: 5000 tokens → $0.10
• Turn 50: 25000 tokens → $0.50
• Turn 100: 50000 tokens → $1.00+ per message

Recommended fix:

Imported: Set hard cost limits

class CostLimitedAgent: MAX_COST_PER_TASK = 1.00 # USD

def __init__(self):
    self.total_cost = 0

def before_call(self, estimated_tokens):
    estimated_cost = self.estimate_cost(estimated_tokens)
    if self.total_cost + estimated_cost > self.MAX_COST_PER_TASK:
        raise CostLimitExceeded(
            f"Would exceed ${self.MAX_COST_PER_TASK} limit"
        )

def after_call(self, response):
    self.total_cost += self.calculate_actual_cost(response)

Imported: Trim context aggressively

def trim_context(messages, max_tokens=4000): # Keep: system prompt + last N messages # Summarize: everything in between if count_tokens(messages) <= max_tokens: return messages

system = messages[0]
recent = messages[-5:]
middle = messages[1:-5]

if middle:
    summary = summarize(middle)  # Compress history
    return [system, summary] + recent

return [system] + recent

Imported: Use streaming to track costs in real-time

Imported: Alert at 50% of budget, halt at 90%

Demo Works But Production Fails

Severity: CRITICAL

Situation: Moving from prototype to production

Symptoms: Impressive demo to stakeholders. Months of failure in production. Works for the founder's use case, fails for real users. Edge cases overwhelm the system.

Why this breaks: Demos show the happy path with curated inputs. Production means:

• Unexpected inputs (typos, ambiguity, adversarial)
• Scale (1000 users, not 3)
• Reliability (99.9% uptime, not "usually works")
• Edge cases (the 1% that breaks everything)

The methodology is questionable, but the core problem is real. The gap between a working demo and a reliable production system is where projects die.

Recommended fix:

Imported: Test at scale before production

Run 1000+ test cases, not 10

Measure P95/P99 success rate, not average

Include adversarial inputs

Imported: Build observability first

import structlog logger = structlog.get_logger()

class ObservableAgent: def execute(self, task): with logger.bind(task_id=task.id): logger.info("task_started") try: result = self._execute(task) logger.info("task_completed", result=result) return result except Exception as e: logger.error("task_failed", error=str(e)) raise

Imported: Have escape hatches

Human takeover when confidence < threshold

Graceful degradation to simpler behavior

"I don't know" is a valid response

Imported: Deploy incrementally

1% of traffic, then 10%, then 50%

Monitor error rates at each stage

Agent Fabricates Data When Stuck

Severity: HIGH

Situation: Agent can't complete task with available information

Symptoms: Agent invents plausible-looking data. Fake restaurant names on expense reports. Made-up statistics in reports. Confident answers that are completely wrong.

Why this breaks: LLMs are trained to be helpful and produce plausible outputs. When stuck, they don't say "I can't do this" - they fabricate. Autonomous agents compound this by acting on fabricated data without human review.

The agent that fabricated expense entries was trying to meet its goal (complete the expense report). It "solved" the problem by inventing data.

Recommended fix:

Imported: Validate against ground truth

def validate_expense(expense): # Cross-check with external sources if expense.restaurant: if not verify_restaurant_exists(expense.restaurant): raise ValidationError("Restaurant not found")

# Check for suspicious patterns
if expense.amount == round(expense.amount, -1):
    flag_for_review("Suspiciously round amount")

Imported: Require evidence

system_prompt = ''' For every factual claim, cite the specific tool output that supports it. If you cannot find supporting evidence, say "I could not verify this" rather than guessing. '''

Imported: Use structured outputs

from pydantic import BaseModel

class VerifiedClaim(BaseModel): claim: str source: str # Must reference tool output confidence: float

Imported: Detect uncertainty

Train to output confidence scores

Flag low-confidence outputs for human review

Never auto-execute on uncertain data

Integration Is Where Agents Die

Severity: HIGH

Situation: Connecting agent to external systems

Symptoms: Works with mock APIs, fails with real ones. Rate limits cause crashes. Auth tokens expire mid-task. Data format mismatches. Partial failures leave systems in inconsistent state.

Why this breaks: The companies promising "autonomous agents that integrate with your entire tech stack" haven't built production systems at scale. Real integrations have:

• Rate limits (429 errors mid-task)
• Auth complexity (OAuth refresh, token expiry)
• Data format variations (API v1 vs v2)
• Partial failures (webhook received, processing failed)
• Eventual consistency (data not immediately available)

Recommended fix:

Imported: Build robust API clients

from tenacity import retry, stop_after_attempt, wait_exponential

class RobustAPIClient: @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60) ) async def call(self, endpoint, data): response = await self.client.post(endpoint, json=data) if response.status_code == 429: retry_after = response.headers.get("Retry-After", 60) await asyncio.sleep(int(retry_after)) raise RateLimitError() return response

Imported: Handle auth lifecycle

class TokenManager: def init(self): self.token = None self.expires_at = None

async def get_token(self):
    if self.is_expired():
        self.token = await self.refresh_token()
    return self.token

def is_expired(self):
    buffer = timedelta(minutes=5)  # Refresh early
    return datetime.now() > (self.expires_at - buffer)

Imported: Use idempotency keys

Every external action should be idempotent

If agent retries, external system handles duplicate

Imported: Separate read/write permissions

Agent can read anything

Write requires explicit approval

Imported: Dangerous actions require confirmation

DANGEROUS_ACTIONS = [ "delete_*", "send_email", "transfer_money", "modify_production", "revoke_access", ]

async def execute_action(action): if matches_dangerous_pattern(action): approval = await request_human_approval(action) if not approval: return ActionRejected(action) return await actually_execute(action)

Imported: Dry-run mode for testing

Agent describes what it would do

Human approves the plan

Then agent executes

Imported: Audit logging for everything

Every action logged with context

Who authorized it

What changed

How to reverse it

Agent Runs Out of Context Window

Severity: MEDIUM

Situation: Long-running agent tasks

Symptoms: Agent forgets earlier instructions. Contradicts itself. Loses track of the goal. Starts repeating itself. Model errors about token limits.

Why this breaks: Every message, observation, and thought consumes context. Long tasks exhaust the window. When context is truncated:

• System prompt gets dropped
• Early important context lost
• Agent loses coherence

Recommended fix:

Imported: Use external memory

Don't keep everything in context

Store in vector DB, retrieve when needed

See agent-memory-systems skill

Imported: Hierarchical summarization

Recent: full detail

Medium: key points

Old: compressed summary

Can't Debug What You Can't See

Severity: MEDIUM

Situation: Agent fails mysteriously

Symptoms: "It just didn't work." No idea why agent failed. Can't reproduce issues. Users report problems you can't explain. Debugging is guesswork.

Why this breaks: Agents make dozens of internal decisions. Without visibility into each step, you're blind to failure modes. Production debugging without traces is impossible.

Recommended fix:

Imported: Structured logging

import structlog

logger = structlog.get_logger()

class TracedAgent: def think(self, context): with logger.bind(step="think"): thought = self.llm.generate(context) logger.info("thought_generated", thought=thought, tokens=count_tokens(thought) ) return thought

def act(self, action):
    with logger.bind(step="act", action=action.name):
        logger.info("action_started")
        try:
            result = action.execute()
            logger.info("action_completed", result=result)
            return result
        except Exception as e:
            logger.error("action_failed", error=str(e))
            raise

Imported: Use LangSmith or similar

from langsmith import trace

@trace def agent_step(state): # Automatically traced with inputs/outputs return next_state

Imported: Save full traces

Every step, every decision

Inputs and outputs

Latency at each step

Token usage

Imported: Validation Checks

Agent Loop Without Step Limit

Severity: ERROR

Autonomous agents must have maximum step limits

Message: Agent loop without step limit. Add max_steps to prevent infinite loops.

No Cost Tracking or Limits

Severity: ERROR

Agents should track and limit API costs

Message: Agent uses LLM without cost tracking. Add cost limits to prevent runaway spending.

Agent Without Timeout

Severity: WARNING

Long-running agents need timeouts

Message: Agent invocation without timeout. Add timeout to prevent hung tasks.

MemorySaver Used in Production

Severity: ERROR

MemorySaver is for development only

Message: MemorySaver is not persistent. Use PostgresSaver or SqliteSaver for production.

Long-Running Agent Without Checkpointing

Severity: WARNING

Agents that run multiple steps need checkpointing

Message: Multi-step agent without checkpointing. Add checkpointer for durability.

Agent Without Thread ID

Severity: WARNING

Checkpointed agents need unique thread IDs

Message: Agent invocation without thread_id. State won't persist correctly.

Using Agent Output Without Validation

Severity: WARNING

Agent outputs should be validated before use

Message: Agent output used without validation. Validate before acting on results.

Agent Without Structured Output

Severity: INFO

Structured outputs are more reliable

Message: Consider using structured outputs (Pydantic) for more reliable parsing.

Agent Without Error Recovery

Severity: WARNING

Agents should handle and recover from errors

Message: Agent call without error handling. Add try/catch or error handler.

Destructive Actions Without Rollback

Severity: WARNING

Actions that modify state should be reversible

Message: Destructive action without rollback capability. Save state before modification.

Imported: Collaboration

Delegation Triggers

• user needs multi-agent coordination -> multi-agent-orchestration (Multiple agents working together)
• user needs to test/evaluate agent -> agent-evaluation (Benchmarking and testing)
• user needs tools for agent -> agent-tool-builder (Tool design and implementation)
• user needs persistent memory -> agent-memory-systems (Long-term memory architecture)
• user needs workflow automation -> workflow-automation (When agent is overkill for the task)
• user needs computer control -> computer-use-agents (GUI automation, screen interaction)

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.