AI Agent Infrastructure Platform

Build, Deploy & Operate AI Agents in Production.
Powered by Runtime Guardrails.

Create AI agents in minutes, upload your own knowledge, connect your preferred AI provider, generate an API Key, and monitor every execution with built-in-runtime guardrails and observability. See every mission, understand every decision, prevent runaway costs, and confidently operate AI agents at scale.

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Built for modern AI agent frameworks

LangGraph•CrewAI•AutoGen•OpenAI Agents•Semantic Kernel•Custom Agent Systems
100% Trace Visibility
Budget Guardrails
Mission-Level Monitoring
Production-Ready Governance

Engineered for Complex AI Architectures

Click through the control panels below to see exactly how AgentPulse tracks and manages live agent missions.

app.agentpulse.com/workspaces/default/overview
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AgentPulse Main Dashboard

Everything You Need To Run AI Agents In Production

Deep structural diagnostic tools engineered to remove operational uncertainty from live LLM workflow pipelines.

Runtime Observability

Track executions, tool calls, latency, token usage, and failures in real time.

Safe Execution Controls

Prevent infinite loops, runaway agents, and unsafe actions before they become incidents.

Budget Guardrails

Set spend limits, alerts, and automated controls across agents and teams.

Agent Mission Tracking

Follow every mission from start to completion with full execution history.

Audit Logs

Maintain accountability with searchable logs and governance records.

BYOK Support

Use your own OpenAI, Anthropic, Gemini, Groq, or custom provider credentials.

Multi-Agent Monitoring

Monitor workflows involving multiple agents, tools, and orchestration systems.

RAG Attribution Tracing

See exactly which sources influenced every response and decision.

Developer Documentation

Create an agent, copy its API key, configure your AI provider if needed, and follow the examples below to integrate AgentPulse into your application.

Integration Flow Architecture
Application ──► Send Request to AgentPulse ──► Authenticate Agent API Key
                                                    │
Display Response in Application ◄── Receive Final AI Response ◄── Poll Status Endpoint ◄── Return step_id
Steps 1 & 2

Send request using your Agent API Key for authentication.

Steps 3, 4 & 5

Platform retrieves configuration, executes the task, and returns a step_id.

Steps 6 & 7

Poll the status endpoint until the task is complete, then read output_data.result.

Step 8

Isolate and securely display the clean response string in your application frontend.

💡 Free Tier Operations: Utilize the light REST parameters configuration by dispatching to POST /steps/execute.
Required Authentication Header

Every incoming request payload requires your unique validation string map to be authorized by the telemetry gateway:

X-API-Key: <your_agent_api_key>
How to Display the Response

The AI response is returned by AgentPulse through the API. Your application is responsible for displaying that response to the end user. AgentPulse manages execution, monitoring, and runtime protection, while your frontend determines how the response is presented.

POST /steps/execute
import requests
import uuid

BACKEND_URL = "https://api.agentpulseai.dev"
AGENT_API_KEY = "ap_agent_xxxxxxxxxxxxxxxxx"

# =========================================================================
# Example 1: Infrastructure Telemetry (no prompt)
# Use this configuration to trace background agent loops without AI model costs
# =========================================================================
infra_payload = {
    "task_name": "Telemetry-Sync-Run",
    "input_data": {},                     # Empty object skips model execution and logs raw trace
    "idempotency_key": str(uuid.uuid4())  # Unique identifier token to avoid duplicate retries
}

# =========================================================================
# Example 2: AI Inference (with prompt)
# Use this configuration to trigger active AI model processing via your BYOK keys
# =========================================================================
inference_payload = {
    "task_name": "Market-Analysis-Stream",
    "input_data": {
        "prompt": "Analyze high-throughput real-time AI cluster workloads"
    },
    "idempotency_key": str(uuid.uuid4())
}

# Dispatch request to the Free Tier REST gateway
response = requests.post(
    f"{BACKEND_URL}/steps/execute",
    headers={
        "X-API-Key": AGENT_API_KEY,
        "Content-Type": "application/json"
    },
    json=infra_payload # Swap with inference_payload to execute active AI model processing
)

print(response.json())

Why AgentPulse?

Most AI tools help you build agents. AgentPulse helps you operate them.

Build Agents

Prompt Engineering
Agent Frameworks
Workflow Builders
Model Providers
RAG Pipelines

Operate Agents

Execution Visibility
Runtime Monitoring
Cost Governance
Failure Detection
Mission Analytics
Safety Controls
Audit Trails
Production Operations

Building an AI agent is only the beginning.
Running it safely, reliably, and cost-effectively in production is where AgentPulse comes in.

Built For Teams That Need Reliability

Whether you're an individual developer deploying your first agent or an enterprise operating thousands of missions, AgentPulse provides the visibility and controls needed to run AI systems in production.

Role-Based Access Control (RBAC)
Workspace Management
Audit Trails
BYOK Security
Mission History
Cost Analytics
Governance Policies
Team Collaboration

Deploy Agents With Confidence

Get complete visibility into agent behavior, costs, execution paths, and safety controls before production issues become expensive incidents.

Start Free

No credit card required • Setup in minutes • BYOK supported

Treat AI Agents Like
Production Infrastructure

Stop managing autonomous execution flows as black-box prompt strings. Gain absolute execution trace telemetry, stability runtime governance, and real-time step failure orchestration controls right now.