Autonomous Agent Activation

Traditional agents live in a constant loop of inquiry – polling stock prices every few seconds, checking inventory levels on a schedule, querying databases to see if anything changed since the last check.

This is especially acute for LLM-based agents. They have vast knowledge but limited awareness of the present. Every activation requires reconstructing context from scratch – fetching current state, assembling relevant history, building the prompt that will let them reason about now.

This pattern made sense when agents were simple automations - scripts that ran periodically and performed fixed actions. But modern agents are different: reasoning systems capable of nuanced decisions that should be acting on opportunities, not hunting for them.

Yet we've inherited the polling model because it's what we know. And it comes with real costs.

The latency gap. There's always a delay between when a condition becomes true and when the agent notices. If you poll every minute, you can be up to 59 seconds late. Poll every second, and you've traded latency for load. The math never works in your favor.

Wasted compute. Most polls return nothing interesting. Your agent asks "has anything changed?" thousands of times, and the answer is usually "no." You're paying for CPU cycles, API calls, and database queries to repeatedly confirm that nothing happened.

Orchestration complexity. When multiple agents need to coordinate, you build elaborate choreography. Message queues, state machines, retry logic, dead letter handling. The infrastructure to manage polling becomes its own system to maintain.

Context reconstruction. Every time an agent polls and finds something interesting, it needs to rebuild its understanding of the situation. What led to this condition? What else is happening right now? What should I consider before acting? The agent spends as much time gathering context as it does reasoning.

Inferal flips the model. Instead of agents constantly asking questions, the data layer watches continuously and activates agents when conditions align.

You define what matters - a rule that describes the pattern you care about:

• Inventory dropping below safety thresholds while demand is accelerating
• Transaction sequences matching known fraud signatures
• Customer behavior indicating churn risk combined with high lifetime value
• Sensor readings crossing tolerance bands while maintenance is overdue

The system evaluates these patterns continuously against your data streams. When a rule becomes satisfied, Inferal doesn't just send a notification. It activates the agent with full context already assembled.

The agent receives everything it needs: the facts that triggered the condition, the historical context, related entities, and any derived calculations. No queries required. No context reconstruction. The agent can immediately reason about what to do.

For LLM agents, this is transformative. Instead of expensive context reconstruction on every activation – the RAG lookups, the database queries, the prompt assembly – the agent receives precisely the facts that triggered the condition. The context window contains exactly what matters, already structured for reasoning.

This isn't just an optimization - it changes how you design agent systems.

Agents respond to situations, not signals. Your code doesn't wire up event streams or manage polling. It activates when a pattern becomes true, with full context already assembled.

Timing becomes declarative. Instead of configuring poll intervals and batching strategies, you express timing requirements in your rules. "Within 5 seconds of condition" or "before end of trading window" become part of the pattern definition.

Coordination happens in the data layer. Multiple agents can be triggered by related conditions without complex orchestration. The same facts that activate one agent can activate others - or be withheld until a coordination condition is met.

Every decision, on the record. Because the agent knows which facts triggered its activation, it can explain why it acted. Every activation, every rule insertion, every action - traceable. Auditors don't have to reconstruct the sequence from logs. The causal chain is part of the activation itself. Intelligence you can observe is intelligence you can trust.

Most agent architectures treat activation as something external - a scheduler, a queue, a cron job. The agent itself has no say in when it runs next.

But what if an agent could schedule its own reactivation?

With Inferal, agents can insert new rules during execution. An agent processing a customer complaint might insert a rule to reactivate in 48 hours if no resolution is logged. A trading agent might set conditions to wake when volatility crosses a threshold it calculated. A monitoring agent might define new patterns based on anomalies it discovered.

This isn't just convenience - it's a fundamental shift in agency. The agent doesn't depend on external orchestration to follow up. It doesn't need a separate scheduler to track pending actions. It defines its own conditions and goes dormant, trusting the system to wake it when those conditions are met.

The dormant state becomes intentional. Like an intelligence that activates only when needed - methodical, purposeful - the agent isn't idle. It's watching through the rules it created.

The pattern you write isn't just a query - it's a standing order. The system watches for matches continuously without you managing the watching.

When your business logic changes, you update the pattern. When you add new data sources, existing patterns automatically consider them if they're relevant. When you need a new kind of activation, you write a new rule.

The infrastructure handles the when and how. You focus on what matters.