Features
Production-grade infrastructure for every aspect of your agent workloads.
Durable Agent Execution
Agents are long-running processes that interact with external systems over hours or days. Unlike HTTP requests, they can't simply be retried. The Agent Engine checkpoints execution state so agents survive crashes, deployments, and infrastructure changes without losing progress.
The problem
When an agent crashes mid-task, traditional infrastructure loses all progress. The agent restarts from scratch, repeating expensive LLM calls and external API interactions.
Why it matters
Production agents must be resilient. Durable execution means no lost work, no repeated costs, and no manual recovery — even during deployments and infrastructure changes.
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Execution Tracing
Agents make hundreds of decisions per run — tool calls, LLM queries, branching logic. Without tracing, debugging is guesswork. Every step is automatically captured with full context: inputs, outputs, latency, token usage, and decision paths.
The problem
An agent produces unexpected results and nobody knows why. Without structured traces, teams resort to log grepping and guesswork to understand what happened.
Why it matters
Full tracing turns agent behavior from a black box into an auditable, debuggable system. Essential for compliance, quality assurance, and continuous improvement.
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Replay & Debugging
When an agent produces unexpected results, you need to understand exactly what happened. Replay any historical run step-by-step, inspect state at each checkpoint, and identify where behavior diverged from expectations.
The problem
Reproducing agent failures requires recreating the exact conditions — the same state, the same external responses, the same timing. In practice, this is nearly impossible.
Why it matters
Replay makes every agent run reproducible. Debug production issues without guessing, validate fixes against real scenarios, and build confidence in agent behavior.
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Cost Guardrails
A single agent can consume thousands of dollars in LLM tokens if left unchecked. Set per-agent budgets, per-run token limits, and organization-wide spending policies. Get alerts before costs escalate, not after.
The problem
An agent enters a loop or encounters an edge case that triggers excessive LLM calls. By the time anyone notices, the bill is already significant.
Why it matters
Predictable costs are a requirement for production deployments. Guardrails enforce spending limits automatically, making agent workloads financially sustainable.
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Stateful Agent Workflows
Agents performing real work need to maintain context across interactions, tool calls, and time. The Agent Engine provides durable state management — agents pick up where they left off, even after interruptions.
The problem
Stateless agents lose context between interactions. Each new request starts from scratch, leading to repetitive work, inconsistent behavior, and poor user experience.
Why it matters
Real-world tasks span multiple interactions and time periods. Stateful workflows let agents maintain continuity — essential for complex, multi-step business processes.
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Multi-Agent Coordination
Complex tasks require multiple specialized agents working together. Route tasks between agents, share execution context, manage dependencies, and prevent conflicts — all without custom orchestration code.
The problem
Building custom orchestration for multi-agent systems is complex, error-prone, and hard to debug. Teams spend more time on plumbing than on agent logic.
Why it matters
Multi-agent systems are how real work gets done at scale. Built-in coordination means teams can compose specialized agents into powerful workflows without infrastructure overhead.
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Tool & API Integration
Agents interact with dozens of external systems: APIs, databases, SaaS platforms, internal tools. The Agent Engine provides a unified tool framework with automatic retry, rate limiting, authentication management, and error handling.
The problem
Each external integration requires custom retry logic, error handling, rate limiting, and auth management. This boilerplate code is duplicated across every agent.
Why it matters
A unified tool framework eliminates integration boilerplate, reduces bugs, and ensures consistent behavior across all external interactions.
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Model-Agnostic Architecture
Swap LLM providers without rewriting agent logic. The Agent Engine abstracts the model layer so you can use OpenAI, Anthropic, open-source models, or your own fine-tuned models interchangeably.
The problem
Agent logic becomes tightly coupled to a specific LLM provider's API. Switching providers means rewriting code, not just changing a configuration.
Why it matters
The LLM landscape evolves rapidly. Model-agnostic architecture gives you freedom to adopt the best model for each task without vendor lock-in or code rewrites.
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