brillio
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An AI Agent Orchestration Platform bridges probabilistic AI agents and deterministic enterprise APIs through controlled execution, middleware abstraction, and asynchronous request handling. It prevents autonomous agents from overwhelming legacy systems by separating agent reasoning from backend transactions.
The difficult part is that foundation models can retry, reinterpret errors, and generate additional tool calls, while SAP, Oracle, and mainframe APIs operate under strict contracts and rate limits.
Where the friction appears:
The Brillio ADAM approach provides a useful reference for thinking about enterprise application connectors, middleware abstraction layers, and distributed application state management.
A practical implementation would isolate legacy backends from raw agent tool-calling loops using:
For those building agent integrations with legacy ERPs, how are you implementing backpressure and rate-limiting for autonomous agents without sacrificing throughput or allowing retries to overwhelm the backend?
The difficult part is that foundation models can retry, reinterpret errors, and generate additional tool calls, while SAP, Oracle, and mainframe APIs operate under strict contracts and rate limits.
Where the friction appears:
- legacy ERP API throttling: Multiple agents can exhaust API quotas or trigger service blocks when they call the same backend concurrently.
- token cost spirals: Repeated reasoning, failed requests, and retry loops increase inference costs without necessarily completing business operations.
- custom middleware tech debt: Point-to-point integrations create duplicated validation, inconsistent retry logic, and difficult-to-maintain connectors.
- State consistency: Agents need reliable responses and current schemas, even when backend transactions are delayed or rejected.
The Brillio ADAM approach provides a useful reference for thinking about enterprise application connectors, middleware abstraction layers, and distributed application state management.
A practical implementation would isolate legacy backends from raw agent tool-calling loops using:
- Event-driven message queuing with bounded concurrency, backpressure, and per-system rate limits.
- Semantic caching for reusable read results, with explicit invalidation rules for changing enterprise data.
- Schema validation and typed connectors to reject malformed requests before they reach backend systems.
- Idempotency keys, bounded retries, and dead-letter queues to prevent retry storms and duplicate transactions.
For those building agent integrations with legacy ERPs, how are you implementing backpressure and rate-limiting for autonomous agents without sacrificing throughput or allowing retries to overwhelm the backend?