DEV Community: Heelo

Beyond Simple Retries: Designing Reliable Recovery for AI Agent Tool Execution

Heelo — Thu, 29 Oct 2026 04:41:16 +0000

What happens when an AI agent calls an external API and the request times out?

The obvious solution seems simple: retry.

But what if the operation already succeeded?

This is one of the most interesting challenges in building reliable AI agents.

The Problem With Blind Retries

Imagine an AI agent responsible for sending an email.

The agent sends the request to an email API.

The email is successfully delivered, but the API response is lost because of a network timeout.

The agent assumes the operation failed and retries.

Now the recipient receives the same email twice.

The problem isn't that the agent misunderstood the user's request.

The problem is that the agent couldn't determine whether the previous action had succeeded.

Three Possible Execution States

After a tool call, an agent should distinguish between:

Success: The operation completed and the result is confirmed.

Failure: The operation definitely failed.

Unknown: The system cannot determine whether the operation completed.

The third state is especially important.

A timeout does not necessarily mean failure.

Why Idempotency Matters

An idempotent operation can be repeated without producing additional unintended effects.

For example, an API that supports idempotency keys can recognize repeated requests and avoid creating duplicate operations.

However, not every external API supports this functionality.

Developers may also need application-level execution tracking and verification.

A Simplified Recovery Strategy

Instead of immediately retrying every failed request, an agent could follow this process:

Execute the tool operation.

Record the execution identifier and available state.

Detect whether the result is confirmed, failed, or unknown.

For uncertain outcomes, check the external system when possible.

Retry only when the operation can be repeated safely.

Verify the final result.

If the outcome cannot be determined reliably, the system may need to report uncertainty and request user guidance.

What We're Exploring at HEELO

At HEELO, we're building an execution layer for AI agents.

Our development and local diagnostic testing have included investigating tool execution failures and unintended repeated actions.

These scenarios demonstrate why agent reliability requires more than successful planning or basic API integration.

We're particularly interested in how execution tracking, verification, and recovery mechanisms can improve multi-step workflows.

HEELO is still under active development and testing.

The Bigger Picture

Building an AI agent that can call tools is relatively straightforward compared with making those actions dependable.

A reliable agent needs to understand not only what it should do next, but also what may have already happened.

Retrying is a mechanism. Recovery is a strategy.

That's an important distinction for real-world AI systems.

HEELO — From Talking to Doing.

https://heelo.dev

Discussion

How do you handle uncertain execution states in your AI agent systems?

Do you rely on idempotency keys, execution logs, read-after-write verification, or another recovery strategy?

Stop Guessing: How to Handle Missing Parameters in AI Agent Tool Calls

Heelo — Wed, 28 Oct 2026 02:25:54 +0000

One of the most overlooked challenges in building AI agents isn't planning.

It's knowing when not to execute a tool call.

Consider this user request:
"Schedule a meeting with Alex tomorrow."

The intent is clear, but several important parameters may be missing:

What time should the meeting start?
How long should it last?
Which calendar should be used?
Is Alex's contact information available? If an agent guesses these values, it might schedule the wrong meeting.

If it calls the calendar API with incomplete arguments, the operation might fail entirely.

So how should developers handle this?

Validate Before Execution A reliable tool-calling workflow should distinguish between three situations:

Ready to execute: All required parameters are available, and the action is authorized.

Needs clarification: Information required for execution is missing or ambiguous.

Cannot proceed: The request is invalid, unsupported, or unauthorized.

This decision should happen before executing a state-changing operation.

A Simple Parameter Validation Example Suppose a scheduling tool requires a participant, start time, and end time.

We can represent those requirements using Pydantic:
from datetime import datetime
from pydantic import BaseModel, ValidationError, model_validator

class MeetingRequest(BaseModel):
participant: str
start: datetime
end: datetime

@model_validator(mode="after")
def check_time_range(self):
    if self.end <= self.start:
        raise ValueError("End time must be after start time")
    return self

def validate_request(data):
try:
request = MeetingRequest.model_validate(data)
return {"status": "ready", "request": request}
except ValidationError as exc:
return {
"status": "needs_review",
"errors": exc.errors(include_url=False)
}
The validation step detects missing fields and invalid time ranges.

However, validation errors should not automatically trigger a tool retry.

The agent must first determine whether the error can be resolved from authorized context or whether it needs to ask the user.

Add a Clarification Step For example, the initial request might contain: { "participant": "Alex", "start": null, "end": null } Instead of inventing meeting times, the agent should ask: "What time would you like to meet Alex, and how long should the meeting last?"

After receiving the answer, it can rebuild the request and validate it again.

A simplified workflow looks like this:

User Request → Extract Parameters → Validate → Clarify if Needed → Authorize → Execute → Verify

This helps separate natural-language interpretation from external execution.

Avoid Over-Clarification Asking questions isn't always necessary.

An agent may be able to use an existing preference, such as a previously authorized default meeting duration.

But there's an important difference between:

Applying an authorized user preference
Retrieving an existing value from trusted context
Guessing an unknown parameter An agent should not silently convert uncertain assumptions into confirmed facts.

Good clarification logic is selective, not excessive.

Verification Still Matters Even a fully validated tool call can fail.

An API may time out, a calendar event may be rejected, or the wrong external state may be updated.

That's why parameter validation is only one part of reliable execution.

The workflow also needs error handling, authorization checks, and outcome verification.

What We're Exploring at HEELO At HEELO, we're developing an execution layer for AI agents, with a focus on tool integration, multi-step workflows, and reliability testing.

Through local diagnostic testing, we've investigated failure scenarios involving missing calendar parameters.

These experiences highlight why agent systems need careful validation before interacting with external tools.

HEELO is still under development, and these design considerations are part of our ongoing exploration.

A reliable AI agent shouldn't just know how to call a tool. It should know when it has enough information to call it correctly.

🚀 HEELO — From Talking to Doing.

Website: https://heelo.dev

Discussion
How do you handle missing tool parameters in your AI agent projects?

Do you use JSON Schema validation, Pydantic, LLM-based clarification, or a combination of these approaches?

I'd love to hear what has worked in your implementations.

Designing Human Approval Gates for AI Agent Workflows

Heelo — Mon, 26 Oct 2026 16:41:27 +0000

One important problem in agentic AI is deciding when an agent should execute a tool call automatically and when it should request human approval.

A system that requires confirmation for everything can become unusable.

But a system that executes consequential actions without adequate authorization can be dangerous.

How should developers approach this trade-off?

Separate Planning From Execution

An AI agent might generate a valid plan without being authorized to execute every step.

For example:

Read a calendar.

Identify scheduling conflicts.

Propose a new meeting time.

Cancel an existing event.

Send notifications to attendees.

The first three steps may be relatively low-risk in an authorized context.

The final two modify external state and affect other people.

The system should evaluate execution permissions before performing these operations.

Add an Approval Gate

A simplified architecture might look like this:

User Request → Planner → Tool Selection → Policy Check → Execution → Verification

The policy check can evaluate:

Whether the requested operation is authorized

The potential impact of the action

Whether the action is reversible

Whether required parameters are present

Whether explicit confirmation is necessary

The policy engine should not rely solely on an LLM's judgment for security-critical decisions.

Example: A Simple Approval Policy

The following pseudocode illustrates the idea:

def should_request_approval(action, permissions):
if not permissions.allows(action):
return "DENY"

if action.is_high_impact:
    return "ASK_USER"

if action.has_external_side_effects:
    return "CHECK_POLICY"

return "EXECUTE"

This is illustrative pseudocode, not production-ready access-control logic.

Real systems need to consider tool-specific permissions, authentication, approval expiration, auditability, and race conditions.

Approval Is Not the End of the Workflow

Even after the user approves an operation, execution can still fail.

The API might time out.

The parameters could be incorrect.

A retry could unintentionally duplicate an action.

That's why approval mechanisms should be combined with execution monitoring, safe retry handling, and outcome verification.

What We're Exploring at HEELO

At HEELO, we're developing an execution layer for AI agents.

Our work includes tool integration, multi-step workflows, and reliability testing.

As agents gain the ability to act across external systems, handling authorization and user control becomes an important design consideration.

Reliable agents need to know not only how to execute an action, but whether they should execute it.

HEELO — From Talking to Doing.

Project: https://heelo.dev

Discussion

How do you handle approval requirements in your AI agent architecture?

Do you use tool-level permissions, risk-based policies, explicit user confirmation, or a combination of these approaches?

Where AI Agent Tests Go Wrong: From Tool Calls to Real-World Verification

Heelo — Sun, 25 Oct 2026 20:50:25 +0000

Building an AI agent that can call external tools is one challenge.

Building an agent that can reliably complete real-world tasks is another.

A test might pass because a function executed or a failure was correctly reproduced. But that doesn't necessarily mean the user's intended task was completed.

Let's examine where AI agent workflows can fail.

1. Understanding the Execution Pipeline

A typical tool-using agent might follow this sequence:

User Intent → Planner → Tool/API → Verifier → Observed Outcome

Each stage introduces potential failure points.

Missing Parameters

The agent may attempt to execute an operation without providing all the required arguments.

For example, a calendar tool might expect both a start time and an end time.

Missing one of these values can cause an exception or prevent the operation from completing.

API Timeouts

An API request might time out even after the external service has processed it.

The agent must distinguish between a failed request and an uncertain execution state.

Silent Failures

A tool might return a response that appears successful even though the intended real-world change didn't occur.

A response status alone may not be sufficient evidence of task completion.

Duplicate Actions

Retrying an operation without checking previous execution results can produce unintended repeated actions.

This is especially important for tools that send emails, create events, or modify external data.

2. Why PASS/FAIL Can Be Misleading

Consider a diagnostic test written to reproduce a known failure.

If the expected exception occurs, the diagnostic test may be marked as passed.

However, the underlying application behavior remains defective.

This creates two different definitions of success:

Diagnostic success: The test successfully reproduced or detected the expected behavior.

User-task success: The agent actually completed the user's request correctly.

These results should be reported separately.

3. What We Explored Through HEELO Testing

At HEELO, we've investigated tool-execution reliability through local diagnostic tests.

Our testing has included scenarios involving missing calendar parameters and unintended repeated email actions.

These diagnostic cases illustrate why execution evidence matters.

A useful test report should capture:

The original test scenario
Tool calls and input parameters
Relevant execution traces
Observed failures
Whether the user's intended outcome was achieved
Evidence supporting the result

The objective is to distinguish successful defect detection from successful product execution.

4. Moving Toward Outcome-Based Evaluation

Reliable agent evaluation should ask more than whether an API returned successfully.

It should also consider:

Outcome verification: Can the resulting state be checked?

Execution correctness: Were the appropriate tools and parameters used?

Recovery behavior: Can the system recover safely from timeouts and partial failures?

Traceability: Can developers reconstruct what actually happened?

For state-changing operations, idempotency and confirmation mechanisms can also reduce unintended duplicate actions.

5. From Talking to Doing

At HEELO, we're developing an execution layer for AI agents.

Our focus includes tool integration, multi-step workflows, and the testing required to make agent execution more reliable.

The system is still under development, and evaluating failure scenarios is part of that work.

Ultimately, agent reliability should be measured not only by whether an operation ran, but by whether it achieved the correct and verifiable outcome.

HEELO — From Talking to Doing. 🚀

Learn more: https://heelo.dev

Discussion

For developers building tool-using AI agents:

What's the most difficult part of ensuring reliable execution?

Tool selection and parameter validation?
Timeouts and retries?
End-to-end outcome verification?
Debugging multi-step workflows?

I'd be interested to hear how others approach these problems.

AI Agents vs. Chatbots: Why Tool Execution Changes Everything

Heelo — Sat, 24 Oct 2026 22:34:34 +0000

Chatbots and AI agents are often discussed as if they were completely different technologies.

But the distinction is more nuanced.

Modern language models can generate answers, reason through problems, and even call external tools.

So what actually makes an AI system agentic?

The answer has less to do with how sophisticated its conversations are and more to do with how it manages goals, tools, and execution.

1. From Generating Responses to Executing Tasks

Consider two requests:

Request A: "How can I schedule a meeting using Google Calendar?"

Request B: "Check my availability and schedule a meeting for tomorrow afternoon."

The first request primarily requires generating an explanation.

The second may involve interacting with external systems, retrieving information, resolving missing parameters, and performing an action.

An AI agent designed for the second request needs more than natural language generation.

It needs a reliable way to interact with tools.

2. Tool Calling Isn't the Same as Task Completion

Giving an LLM access to APIs does not automatically make it reliable.

For example, imagine an agent executing a calendar operation:

Identify the user's scheduling intent.
Select the appropriate tool.
Extract required parameters.
Request clarification if information is missing.
Execute the calendar operation.
Verify the outcome.

What happens if step 5 succeeds but the API response times out?

The agent might assume the operation failed and retry it, potentially creating duplicate events.

This illustrates an important distinction:

A successful tool call is not necessarily a successfully completed task.

3. Why Execution Infrastructure Matters

Real-world tool execution introduces challenges that developers must address:

Tool selection: Choosing the appropriate operation for a request.
Parameter validation: Ensuring required information is present.
State management: Tracking progress across multiple steps.
Error handling: Responding to timeouts and failed operations.
Idempotency: Preventing unintended duplicate actions.
Verification: Checking whether the final outcome matches the user's intent.

These capabilities are essential for agents that interact with external applications.

4. What We're Building at HEELO

At HEELO, we're building an execution layer for AI agents.

Our goal is to provide a more consistent interface between AI reasoning systems and real-world tools.

We're exploring how unified tool interfaces, multi-step workflows, clarification handling, and execution tracking can make agent development more manageable.

Reliability is a central part of our development and testing work.

Rather than assuming an operation succeeded simply because an API returned a response, we want to better understand whether the intended task was completed.

We're still actively developing and validating these capabilities.

5. The Real Challenge: Reliable Execution

The future of AI agents won't depend only on how well language models reason.

It will also depend on how reliably they interact with external systems.

Better planning is valuable.

But dependable execution is what makes an AI agent useful beyond conversation.

HEELO — From Talking to Doing. 🚀

Let's Discuss

For developers building AI agents:

What's been more challenging in your projects — improving model reasoning or making tool execution reliable?

We'd love to hear your experiences.

Learn more: https://heelo.dev

Join our community: https://discord.gg/DqtEXzSbE

Why Calendar Automation Is Harder Than It Looks for AI Agents

Heelo — Fri, 23 Oct 2026 16:02:53 +0000

Scheduling a meeting sounds like a simple task.

But for an AI agent, managing a calendar reliably involves much more than generating a time and date.

Consider this request:

"Schedule a meeting with my team next Friday afternoon."

An LLM may understand the user's intention immediately. But executing that request through a calendar API introduces several engineering challenges.

1. Understanding Missing Information

Before creating an event, an agent may need to determine:

Which Friday does the user mean?
What time zone should be used?
How long should the meeting last?
Who needs to be invited?
Which calendar should be updated?

When essential information is missing, guessing can lead to incorrect actions.

A reliable agent should recognize uncertainty and request clarification when necessary.

2. Handling Calendar Conflicts

Even when all required parameters are available, scheduling may not be straightforward.

What if the proposed time conflicts with another meeting?

A robust workflow may need to:

Retrieve existing events.
Check availability.
Identify conflicts.
Suggest alternative times.
Request confirmation before making changes.

This turns a seemingly simple operation into a multi-step workflow.

3. Managing API Failures

External services are not always reliable.

A calendar API might time out, reject an authentication token, or return an unexpected response.

One particularly challenging scenario occurs when an API successfully creates an event but the agent never receives the confirmation.

Retrying the same request without checking the result could create duplicate events.

This is why error handling, execution tracking, and idempotency are important considerations.

4. Why Tool Execution Infrastructure Matters

At HEELO, we're building an execution layer for AI agents.

Our goal is to make interactions between AI reasoning systems and external tools easier to manage.

We're exploring several important capabilities:

Tool integration: Providing consistent interfaces for external operations.
Clarification handling: Supporting workflows when user information is incomplete.
Multi-step execution: Managing tasks involving multiple tool calls.
Error handling: Identifying and responding to execution failures.
Reliability testing: Evaluating whether tool execution produces the expected outcome.

We're actively developing and testing these capabilities. Calendar automation is one example of the challenges we're working to understand and address.

5. Successful API Calls Aren't Always Successful Tasks

One important distinction in agent development is the difference between technical success and user success.

An API request might return a successful status, but that doesn't necessarily mean the user's original task was completed correctly.

For example, a calendar event could be created with the wrong time zone, missing participants, or incorrect duration.

Reliable execution requires evaluating the final outcome, not just checking whether an API call succeeded.

Final Thoughts

AI agents have the potential to make everyday productivity tools much easier to use.

But delivering that experience requires careful engineering around tool integration, error handling, user confirmation, and multi-step execution.

At HEELO, we're exploring how to make that infrastructure more dependable.

From Talking to Doing. 🚀

Let's Discuss

What's been your biggest challenge when building calendar automation or other tool-using AI agents?

Learn more: https://heelo.dev

Join our developer community: https://discord.gg/DqtEXzSbE

Why Tool Execution Is Still Hard for AI Agents

Heelo — Fri, 23 Oct 2026 03:39:05 +0000

AI agents are getting better at understanding instructions, generating plans, and reasoning through complex problems.

But planning a task and actually completing it are two very different things.

For example, consider this simple request:

"Schedule a meeting for Friday afternoon and send a confirmation email."

It sounds straightforward. But for an AI agent, this involves multiple steps, different tools, and several possible failure points.

1. Tool Integration Is Harder Than It Looks

Different services expose different APIs, authentication methods, parameters, and response formats.

An agent may know what the user wants but still struggle to interact with the right tools.

A consistent execution interface could help reduce this complexity.

2. Multi-Step Workflows Introduce New Problems

Many real-world tasks require more than one action.

Creating a calendar event might involve checking availability, confirming details, and calling an external calendar API.

What happens if one step succeeds but the next one fails?

How should the agent recover without repeating actions that have already been completed?

3. Reliability Matters

Giving an agent permission to execute tasks creates new responsibilities.

Developers need to consider:

API failures and timeouts
Missing information
Duplicate actions
Authentication errors
User confirmation before important actions
Whether the final result matches the user's original intent

A successful API response doesn't always mean a task was completed correctly.

4. What We're Building at HEELO

At HEELO, we're building an execution layer for AI agents.

Our goal is to make it easier for AI systems to interact with real-world tools and manage multi-step operations through a more consistent interface.

Our development work focuses on tool integration, execution workflows, clarification handling, error management, and testing.

We're still actively developing and validating these capabilities, and reliability is one of our central priorities.

5. From Talking to Doing

The next generation of AI shouldn't just generate better answers.

It should also help turn those answers into useful, dependable actions.

That's the direction we're exploring at HEELO.

From talking to doing. 🚀

We're curious: What's been your biggest challenge when building tool-using AI agents?

Learn more about HEELO: https://heelo.dev