Dynamic LLM &
Tool Orchestration
Route agent requests to the best-fit LLM or tool dynamically — based on semantic goals, real-time context, and cost constraints — without hardcoding routing logic or managing separate orchestration frameworks.
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Why LLM Orchestration Breaks Down at Scale
Static routing tables, separate API clients, and prompt-based orchestration don't survive contact with production workloads. Agents need infrastructure that routes intelligently, plans safely, and adapts in real time.
Routing Logic Is Hardcoded and Brittle
Most orchestration setups require engineers to manually define which LLM or tool handles which type of request. When requirements change or new models become available, every routing rule must be updated by hand — a maintenance burden that scales poorly.
No Cost-Aware Routing Between Models
Different sub-tasks have wildly different cost profiles. Using a frontier model for a simple classification task wastes budget. Without real-time cost tracking and routing intelligence, agents default to one model for everything — expensive and inefficient.
Multi-Step Workflows Lack Type Safety
When an agent chains multiple LLM calls and tool invocations together, output formats rarely match the expected input of the next step. Without type bridging, workflows fail silently or require brittle hand-written adapters between every step.
No Unified Access to Diverse Tool Ecosystems
A financial analysis agent might need a market sentiment LLM, a real-time stock data API, and a deterministic math tool — all in one workflow. Managing separate clients, auth tokens, and API contracts for each is untenable at scale.
Workflows Cannot Adapt at Runtime
Static orchestration pipelines cannot branch based on intermediate results. If a market sentiment query returns low-confidence data, a static workflow can't fall back to a different model or request human approval before proceeding.
No Verification That Plans Are Safe Before Execution
Multi-step agent plans involving sensitive data operations or high-cost model calls are typically executed speculatively. There is no pre-execution check that a plan is type-safe, policy-compliant, and within budget before it runs.
The Tools That Power Dynamic LLM Orchestration
Discovery routes semantically, Flow orchestrates conditionally, Governor optimizes costs over time, Prism bridges data between steps, and the Conduit SDK unifies everything behind one client.
Discovery
Semantic routing — find the best-fit tool or LLM by goal.
Discovery enables agents to semantically search across every connected tool, LLM, and integration on the server — returning ranked matches with relevance scores. discovery.plan generates verified multi-step workflows with type bridging and cost estimates before execution. This is how agents route requests to the right resource without any hardcoded routing tables.
View Discovery →Flow
Conditional orchestration with human-in-the-loop gates.
Flow orchestrates multi-step workflows across different LLMs and tools with conditional routing, variable references between steps, and optional human approval gates. flow.route dispatches to different tools or LLM endpoints based on runtime values — creating dynamic 'routing paths' based on intermediate results. Every validated plan receives a Cognitive Trust Certificate confirming it is type-safe and policy-compliant before it runs.
View Flow →Governor
Cost-aware routing and continuous performance monitoring.
Governor monitors costs and performance across all LLM and tool usage in real time. Its Reflex cycle evaluates cost and performance patterns every ~30 seconds against symbolic rules — enabling strategic routing decisions like 'use a cheaper model for tasks with confidence score above 0.9.' Progressive efficiency gains compound as Reflection converts experience into reusable routing rules stored in Logic.
View Governor →Prism
Transform and reshape data between orchestration steps.
Prism bridges the output format of one LLM or tool to the expected input of the next step — automatically, in natural language. prism.refract reshapes structured data around a goal with self-healing retries. prism.focus bridges incompatible Semio types between systems (e.g., a CRM lead to a billing customer). This eliminates the brittle hand-written adapters that break multi-step orchestration pipelines.
View Prism →Conduit SDK
Unified client across all LLMs, tools, and integrations.
The Conduit SDK provides a single, production-ready client that connects agents to the full DataGrout Hub — giving access to every connected LLM, tool, and integration through one interface. client.discover() calls Discovery semantically, client.flow accesses Flow workflows, and all tool calls include cost metadata. Available in Python, TypeScript, Rust, Elixir, and Ruby with mTLS and zero-config identity.
View Conduit SDK →Building a Financial Analysis Agent with Dynamic LLM Routing
An AI Platform Engineer needs a financial analysis agent that routes queries to a frontier LLM for sentiment, a real-time stock API for market data, and a deterministic math tool for calculations — dynamically, based on query type and cost constraints.
Semantic routing to the best-fit resource
DiscoveryThe financial analysis agent receives a complex query. Rather than hitting a hardcoded routing table, it calls discovery.discover with the goal in natural language. Discovery returns ranked matches: a frontier LLM for market sentiment analysis, a real-time stock data API, and DataGrout's Math tool for portfolio calculations — with relevance scores and cost estimates for each.
Plan a verified multi-step workflow
Discovery + Flowdiscovery.plan generates a multi-step workflow: (1) run market sentiment LLM, (2) fetch real-time stock data, (3) run Math tool for portfolio calculations, (4) use Prism to merge results. The plan receives a Cognitive Trust Certificate confirming it is type-safe, policy-compliant, and within the agent's credit budget before a single step executes.
Bridge data formats between LLM and tool steps
PrismThe sentiment LLM returns a free-text narrative. Before passing it to the stock data tool, prism.refract reshapes the output into a structured format matching the tool's expected schema — automatically, with self-healing retries if the data shape is unexpected. No hand-written adapters, no brittle intermediate transforms.
Conditional routing based on intermediate results
FlowAfter the sentiment step, flow.route evaluates the confidence score. If confidence is below 0.7, the workflow branches to a secondary, higher-quality LLM for re-analysis before proceeding. If the portfolio calculation suggests a high-risk action, flow.request-approval pauses the workflow for human sign-off — all without any custom control flow code.
Cost optimization compounds over time
GovernorWith Governor active, every tool call and model invocation is tracked against the agent's budget in real time. Governor's Reflection cycle identifies patterns — 'simple classification queries never require the frontier model' — and generates symbolic routing rules stored in Logic. Future Reflex evaluations enforce these rules at sub-10ms latency, progressively reducing token costs without sacrificing accuracy.
What You Get Out of the Box
Semantic routing, verified multi-step planning, data bridging, conditional orchestration, and cost optimization — without building or maintaining a custom orchestration framework.
Semantic routing — no hardcoded logic
Discovery routes requests to the best-fit LLM or tool based on semantic similarity to the agent's goal — not a static routing table. Add new models or tools to your server and they become immediately discoverable without any code changes.
Pre-execution plan verification
discovery.plan generates multi-step orchestration plans with a Cognitive Trust Certificate confirming the plan is type-safe, policy-compliant, cycle-free, and within budget before a single step executes. No more speculative execution of expensive or destructive workflows.
Automatic data bridging between steps
Prism reshapes output formats between LLM calls and tool invocations automatically — using natural language goals, not hand-written adapters. Self-healing retries handle unexpected data shapes without surfacing errors to the agent.
Conditional branching without custom code
flow.route dispatches to different tools, models, or human approval gates based on runtime values from intermediate steps. Complex orchestration logic is declared, not coded — and every workflow is saved as a reusable verified skill.
Cost optimization that compounds over time
Governor tracks costs across every LLM call and tool invocation in real time, then converts observed patterns into symbolic routing rules. Agents progressively route cheaper models to simpler tasks — reducing token spend without sacrificing accuracy.
One client for every LLM and tool
The Conduit SDK provides a single, production-ready interface to every connected LLM, tool, and integration on the server — in Python, TypeScript, Rust, Elixir, and Ruby. Replace separate API clients and auth tokens with one authenticated connection.
Frequently Asked Questions
Common questions about dynamic LLM and tool orchestration with DataGrout.
Ready to orchestrate LLMs dynamically?
Semantic routing, verified multi-step planning, and cost-aware model selection — available now, with no orchestration framework to build or maintain.
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