BABELFISH · AGENTIC-TO-FLOW
Your agent, converted into a deterministic flow.
Agentic-to-Flow is a drop-in proxy for LLM agents. Change one URL and it observes your call patterns, compiles the repetitive steps into a deterministic flow, and runs them locally - cutting latency, cost, and hallucination risk.
How the conversion works
Three stages. Zero code changes. Your agent keeps running while Agentic-to-Flow learns and compiles in the background.
Observe
Routes through the proxy at the edge. Logs structural call patterns - no PII, no payloads. Every LLM invocation is recorded as a typed execution trace with input schemas, output shapes, and latency metadata.
Learn
Clusters repeated call patterns using execution trace analysis. Identifies which calls are deterministic - same input shape always producing the same output. Statistical confidence thresholds prevent premature compilation.
Compile
Generates a deterministic flow that replaces the LLM call. Tests it against the original with canary traffic. Deploys progressively with automatic rollback on any divergence.
Observe
Routes through the proxy at the edge. Logs structural call patterns - no PII, no payloads. Every LLM invocation is recorded as a typed execution trace with input schemas, output shapes, and latency metadata.
Learn
Clusters repeated call patterns using execution trace analysis. Identifies which calls are deterministic - same input shape always producing the same output. Statistical confidence thresholds prevent premature compilation.
Compile
Generates a deterministic flow that replaces the LLM call. Tests it against the original with canary traffic. Deploys progressively with automatic rollback on any divergence.
Architecture
The runtime request path
Every agent call passes through the proxy. If a compiled flow exists and passes validation, it executes locally. Otherwise, the original LLM call proceeds normally.
Continuous Improvement
The learning loop
No training data needed. Agentic-to-Flow observes your agent in production, clusters repeated patterns, generates candidate flows, and validates them through canary testing.
What converts?
Agentic-to-Flow targets the agent steps that are repetitive and deterministic - leaving genuine reasoning to the LLM.
Classification & routing
Intent detection, topic routing, and category assignment. When the same input shapes map to the same labels every time, the LLM call becomes a lightweight local classifier - cutting latency from seconds to single-digit milliseconds.
Structured extraction
Name, address, date, amount - parsed from known formats. The extraction schema is learned from repeated traces and compiled into regex-based or parser-based steps that handle every variation the LLM saw, without the token cost.
Guardrail checks
PII detection, toxicity filters, and format validation. These safety checks follow deterministic rules that an LLM re-derives identically every time. Compiled guardrails run in microseconds with zero hallucination risk.
Format transformation
JSON ↔ XML, markdown → HTML, schema migration. Structural transforms that follow fixed mapping rules are perfect conversion targets. The generated flow handles every edge case the LLM encountered during observation.
Retrieval re-ranking
Static relevance scoring that doesn’t change per query. When retrieval results follow stable scoring patterns, a lightweight compiled ranker applies the same logic without an LLM round-trip - keeping RAG pipelines fast and cheap.
Prompt chains
Multi-step prompts with fixed templates and variable slots. When a chain always follows the same path given the same input type, the entire sequence collapses into a single compiled flow - eliminating multiple LLM round-trips.
Every compiled flow is tested before it runs.
Conversion isn’t enough - it has to be validated. Canary testing, automatic rollback, and continuous drift detection ensure compiled flows stay accurate over time.
Canary testing
Before any compiled flow goes live, 5% of real traffic runs through both the compiled flow and the original LLM call simultaneously. Outputs are compared token-for-token.
Traffic ramps progressively with automatic halt at any stage if divergence exceeds configurable thresholds.
Drift detection
Real-world input distributions change. Compiled flows are continuously monitored by sampling production traffic and comparing compiled outputs against the LLM baseline.
When input patterns drift beyond the observed training distribution, the compiled flow is automatically flagged and traffic is rerouted back to the LLM until re-compilation completes.
One-click rollback
Any compiled flow can revert to the original LLM call in under 1 second. Rollback is atomic - no partial states, no downtime.
Automatic rollback triggers on canary failure, drift alerts, or error-rate spikes. Manual rollback is always available from the dashboard or the CLI.
Deploy your way.
Babelfish runs wherever your agents run. Choose the deployment model that matches your compliance and performance requirements.
Hosted Cloud
Fastest start. Managed infrastructure, zero ops overhead. Data processed in-region.
VPC
Runs inside your cloud account. Your network, your rules, our updates. Supports AWS, GCP, and Azure with Terraform modules.
On-Prem
Air-gapped deployment for regulated industries. Full data sovereignty with no external network dependencies.
Start converting in 5 minutes.
Change one URL. Agentic-to-Flow handles the rest - observing, compiling, and deploying deterministic flows.