AI agents that wake up, check for work, and act — on your schedule, around the clock, without a single prompt.
Most AI tools wait to be asked. ibl.ai agents don't. The Heartbeat system — built into the OpenClaw framework and enterprise-hardened by ibl.ai — gives every agent an internal clock.
Agents wake on configurable schedules (every 30 minutes by default), scan for pending tasks, evaluate conditions, and execute actions autonomously. Cron expressions, event-driven triggers, and threshold-based activation all work out of the box.
This is the difference between a reactive chatbot and a production-grade autonomous agent. Your workflows run while your team sleeps, scales, and focuses elsewhere.
Enterprise operations generate continuous streams of data, alerts, reports, and decisions that demand timely action. When AI systems are purely reactive — waiting for a human to type a prompt — critical windows close, anomalies go undetected, and the burden of orchestration falls back on people.
Teams end up building fragile cron scripts, chaining together brittle integrations, or hiring staff whose sole job is to trigger the next step in a workflow. The result is latency, human error, and an AI investment that only delivers value when someone remembers to use it.
Standard LLM interfaces and GPT-style tools only act when prompted. Between prompts, nothing happens — no monitoring, no escalation, no progress.
Time-sensitive tasks like compliance checks, system health monitoring, and data ingestion pipelines stall until a human intervenes, introducing costly delays.Teams often bolt cron jobs and task queues onto AI tools as an afterthought, creating fragile pipelines that break silently and are difficult to audit or maintain.
Engineering resources are consumed maintaining scheduling glue code rather than building business value, and failures are discovered only after damage is done.Stateless AI systems lose all context between sessions. A scheduled agent that can't remember what it did last run can't reason about trends, changes, or prior decisions.
Agents repeat work, miss incremental changes, and cannot support longitudinal workflows like ongoing research synthesis or multi-day compliance audits.Pure cron scheduling misses event-driven scenarios — a file arriving in a folder, a threshold being breached, or an API returning an unexpected value.
Agents respond on a fixed clock rather than when it matters, creating either over-polling waste or dangerous response latency in dynamic environments.Cloud-native AI scheduling solutions tie autonomous workflows to a single vendor's infrastructure, model choices, and data residency policies.
Organizations in regulated industries — defense, healthcare, finance — cannot meet sovereignty, air-gap, or compliance requirements with vendor-hosted scheduling.Define when an agent wakes using standard cron expressions, interval-based timers, or event-driven triggers. The default pulse is every 30 minutes, but schedules can range from seconds to days. Multiple agents can run on independent schedules within the same deployment.
On each heartbeat, the agent loads its persistent memory state — stored as Markdown files and indexed via SQLite vector and keyword search. It recalls prior actions, outstanding tasks, and contextual history from previous runs without any re-prompting.
The OpenClaw Brain orchestrates a ReAct (Reasoning + Acting) loop. The agent reasons about current state, checks conditions (data thresholds, file changes, API responses, calendar events), and decides whether action is warranted — all autonomously.
When action is required, the agent invokes Skills from the 5,700+ plugin library — running Python, R, or shell code in an isolated sandbox, browsing the web, querying databases, sending alerts, or updating files — all within defense-in-depth security boundaries.
Outputs, decisions, and observations from each run are persisted back to the agent's memory layer. This creates a continuous, auditable log of autonomous activity that informs future heartbeat cycles and supports human review.
The Gateway layer delivers agent outputs — reports, alerts, summaries, escalations — to any of 12+ supported channels including Slack, Teams, email, WhatsApp, or custom webhooks, closing the loop with human stakeholders when needed.
Full cron expression support for time-based scheduling, plus event-driven triggers based on file system changes, API polling results, data thresholds, or external webhooks. Agents act when it matters, not just when the clock ticks.
Each agent maintains state across every heartbeat cycle using Markdown-based persistent memory with SQLite vector and keyword search. Agents remember what they did, what changed, and what still needs attention — enabling longitudinal autonomous workflows.
Scheduled agents execute real code — Python, R, shell, SQL — inside isolated computing environments. Container isolation, network restrictions, resource limits, and audit trails ensure autonomous execution is safe, auditable, and contained.
Deploy multiple agents on independent heartbeat schedules within a single platform instance. Agents can operate in parallel, hand off tasks to one another, or aggregate results — supporting complex, multi-step autonomous pipelines.
The Brain layer is fully model-agnostic. Scheduled agents can use GPT-4, Claude, Gemini, Llama, Mistral, or any custom model. Switch models per agent, per task, or per cost threshold without rewriting scheduling logic.
Every heartbeat cycle — what the agent checked, what it decided, what it executed, and what it produced — is logged with timestamps and stored for audit. Supports compliance, debugging, and human oversight of autonomous operations.
Deploy the Heartbeat system on-premises, in a private cloud, or air-gapped environments. No dependency on vendor scheduling infrastructure. Meets data residency, sovereignty, and compliance requirements for regulated industries.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Agent Activation | Agents only act when a human sends a prompt. No activity between interactions. | Agents wake autonomously on cron schedules or event triggers, acting without any human prompt. |
| Memory Across Runs | Each session starts fresh. No awareness of prior actions, decisions, or observations. | Persistent Markdown memory with vector and keyword search gives agents full recall across every heartbeat cycle. |
| Code Execution in Scheduled Tasks | No real code execution. Scheduled outputs are limited to text generation. | Agents execute Python, R, shell, and SQL in isolated sandboxes during every scheduled run, with full package support. |
| Scheduling Infrastructure | Requires custom cron scripts, task queues, and glue code maintained by engineering teams. | Native Heartbeat system with cron expressions, event triggers, and missed-run recovery — no custom infrastructure required. |
| Security of Autonomous Execution | Autonomous scripts run with broad system access, creating significant security and compliance risk. | Defense-in-depth sandbox isolation (NanoClaw/IronClaw) with network restrictions, resource limits, and full audit trails. |
| Deployment Sovereignty | Scheduling tied to vendor cloud infrastructure. No air-gap or on-premises option. | Fully self-hosted on any infrastructure including air-gapped environments. No external scheduling dependencies. |
| Observability and Audit | No structured record of what autonomous processes did, decided, or produced between human interactions. | Every heartbeat cycle produces a timestamped, structured audit log of agent reasoning, actions taken, and outputs generated. |
Agents only act when a human sends a prompt. No activity between interactions.
Agents wake autonomously on cron schedules or event triggers, acting without any human prompt.
Each session starts fresh. No awareness of prior actions, decisions, or observations.
Persistent Markdown memory with vector and keyword search gives agents full recall across every heartbeat cycle.
No real code execution. Scheduled outputs are limited to text generation.
Agents execute Python, R, shell, and SQL in isolated sandboxes during every scheduled run, with full package support.
Requires custom cron scripts, task queues, and glue code maintained by engineering teams.
Native Heartbeat system with cron expressions, event triggers, and missed-run recovery — no custom infrastructure required.
Autonomous scripts run with broad system access, creating significant security and compliance risk.
Defense-in-depth sandbox isolation (NanoClaw/IronClaw) with network restrictions, resource limits, and full audit trails.
Scheduling tied to vendor cloud infrastructure. No air-gap or on-premises option.
Fully self-hosted on any infrastructure including air-gapped environments. No external scheduling dependencies.
No structured record of what autonomous processes did, decided, or produced between human interactions.
Every heartbeat cycle produces a timestamped, structured audit log of agent reasoning, actions taken, and outputs generated.
Persistent situational awareness without analyst fatigue, operating in air-gapped environments with full data sovereignty.
Continuous regulatory compliance monitoring at scale, reducing manual audit burden and accelerating trial oversight cycles.
Proactive risk identification with sub-hour latency, replacing overnight batch jobs with continuous intelligent monitoring.
Eliminates manual data aggregation for compliance reporting, reducing submission errors and freeing staff for higher-value work.
No critical deadline or regulatory change goes unnoticed, with a full audit trail of when information was retrieved and acted upon.
Continuous literature monitoring at scale, ensuring research teams stay current without manual search overhead.
Reduced mean time to detection and resolution for infrastructure issues, with autonomous remediation handling routine failures without on-call intervention.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.