Oracle Infrastructure for Autonomous Agents

Oracle infrastructure for autonomous agents provides deterministic, programmable data execution for AI agents operating onchain. Learn how Oracle Programs, Data Proxies, and machine-to-machine payment

AI agents can already decide what to do. The hard part is making those decisions executable inside an onchain system—safely, repeatably, and without relying on ad-hoc offchain glue. That requires oracle infrastructure for autonomous agents: a programmable data layer where external state, policy, and payments can be invoked by machines and verified by contracts.

SEDA is programmable oracle infrastructure for autonomous agents. It provides a way for agents to request data, apply deterministic policy, and deliver results onchain through Oracle Programs—so agent workflows can be executed as infrastructure, not as a best-effort integration.

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Definition: Oracle infrastructure for autonomous agents

Definition: Oracle infrastructure for autonomous agents is the programmable data infrastructure that lets AI agents onchain make machine-executable data requests—where (1) data sourcing, (2) policy/logic, and (3) onchain delivery are encoded as deterministic, verifiable execution rather than being handled by manual operators or application-specific scripts.

This category exists because autonomous agents don’t just consume data; they trigger actions that must be replayable, auditable, and constraint-respecting under adversarial conditions.

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What an autonomous agent is in an onchain context

In an onchain system, an “autonomous agent” is not just an LLM behind an API. It is a long-running decision process that can:

• Observe external state (markets, APIs, outcomes, inventories)

• Maintain internal state across steps (positions, budgets, risk limits, task progress)

• Trigger onchain actions (submit transactions, adjust parameters, settle flows)

• Coordinate with other agents/services (tool calls, multi-agent orchestration)

Modern agent frameworks emphasize state persistence, tool calling, and orchestration as first-class primitives (e.g., checkpointing, transitions, error handling, observability).

The infrastructure question is: when an agent decides “do X,” what makes that decision safely executable with the right external inputs and the right constraints?

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Why this category exists: agents require policy, not just data

Traditional oracle design assumes a human-built application consumes a feed and applies logic in application code. Agents invert that assumption:

• The consumer is a machine that triggers requests dynamically.

• The decision loop is multi-step and stateful.

• The system must withstand tool errors, retries, partial execution, and adversarial conditions.

• The “right answer” is often not a single number, but a policy-evaluated result (eligible / in-session / valid window / provenance constraints / fallback rules).

Agent workflows also combine probabilistic reasoning (LLM outputs) with deterministic rules. Agentic systems often explicitly separate deterministic steps (“given the same input, produce the same output”) from probabilistic steps (uncertainty-aware decisions).

Oracle infrastructure for autonomous agents exists to provide the deterministic side of that boundary: external state + policy that can be executed identically by any verifier.

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Why static feeds fail agent workflows

Static feeds are optimized for “read a value” patterns. Agents need “execute a policy and return a result” patterns.

1) Agents need context, not only values

A price feed can tell you a number. It usually cannot tell you:

• Is this value stale relative to the agent’s deadline?

• Is this value valid for this venue/session?

• Which source(s) were used and under what constraints?

• What fallback should apply if the primary source is unavailable?

• What transformation is required for the instrument (e.g., basis, funding, index composition)?

For autonomous execution, the system must distinguish:

• Price: a scalar (or tuple) value.

• Asset context: the policy envelope that explains how that value should be interpreted and whether it is admissible for a given action.

Agents operate on asset context because their actions have consequences (trades, liquidations, settlements, rebalances).

2) Agents need deterministic execution constraints

If an agent requests data as part of a transaction-triggered workflow, the output must be deterministic given the same inputs and policy. Otherwise:

• Two verifiers can disagree.

• Replays become non-identical.

• “Same state, different outcome” breaks automation guarantees.

Multi-agent systems already treat reliability and validation as core design constraints—timeouts, retries, validation of outputs, circuit breakers, and explicit failure handling are necessary to prevent cascades.

Oracle calls inside an agent loop must meet the same bar.

3) Agents need machine-to-machine data requests

Agents don’t want dashboards. They want endpoints and programs:

• “Fetch X with policy Y”

• “Transform X into Z under constraints C”

• “Return proof/metadata sufficient to execute action A”

That is machine-to-machine (M2M) execution: structured requests, structured outputs, deterministic policy.

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What programmable oracle infrastructure enables

Programmable oracle infrastructure is the shift from “publishing values” to “executing oracle logic.”

SEDA’s architectural principle is explicit separation of concerns:

• A raw data provider handles sourcing.

• SEDA Oracle Programs apply logic (policy, session rules, fallback behavior).

• A relayer handles onchain delivery.

That design matters for agent systems because it turns “oracle integration” into an infrastructure layer that an agent can invoke repeatedly with predictable results.

Programmable oracle infrastructure enables:

• Policy-executed results, not just raw values

• Context signaling (validity windows, session state, provenance)

• Deterministic fallbacks when sources fail or diverge

• Composability: agents can chain Oracle Programs as tools in a workflow

• Auditability: logic is inspectable at the oracle layer, not buried in app code

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How agents can deploy Oracle Programs

An agent-deployed Oracle Program is a policy module the agent can reference as part of its own execution plan.

Conceptually, an agent deploys an Oracle Program when it needs one or more of the following:

• A new data source combination (multiple APIs, multiple venues)

• A transformation pipeline (normalization, smoothing, gating, aggregation)

• Deterministic fallback rules

• A context envelope for a specific asset or task class

This mirrors how modern agent frameworks treat tools: you don’t hardcode every tool call; you define reusable components and orchestrate them with state and checkpoints.

What changes with SEDA: the “tool” is not a one-off HTTP call. It is a verifiable oracle program that encodes policy at the infrastructure layer and returns results designed for onchain consumption.

Build an Oracle Program for SEDA FAST

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How agents can execute data requests programmatically

An agent’s onchain execution loop typically looks like:

1. Plan (probabilistic): determine what data is needed and what action might follow.

2. Build (create): Build an Oracle Program on SEDA Fast or SEDA Onchain and deploy it to SEDA Network

3. Request (deterministic interface): call an Oracle Program with parameters.

4. Validate (deterministic): check context constraints (freshness, provenance, session state).

5. Act (onchain): submit the transaction(s) that depend on the oracle output.

6. Checkpoint: store state for replay, retries, or human escalation.

This pattern aligns with orchestration guidance: validate intermediate outputs, surface errors, and avoid cascading failures by enforcing explicit checks and retries at boundaries.

In SEDA, Oracle Programs are the boundary object: they produce outputs that are intended to be consumed by contracts and automation code, not interpreted manually.

Getting Started

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Private API usage via Data Proxies

Many agent workflows require data that is not fully public:

• premium market data APIs

• enterprise internal endpoints

• rate-limited services

• authenticated endpoints requiring key management and policy

Data Proxies are an architectural pattern for agent systems: instead of embedding API secrets inside agents (or contracts), you route requests through controlled proxy infrastructure that can:

• enforce authentication and quotas

• redact or scope sensitive fields

• cache and normalize responses

• attach usage accounting and receipts

In an agent setting, Data Proxies reduce key leakage risk and centralize policy enforcement—while Oracle Programs remain the place where the interpretation of that data is encoded deterministically.

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Machine-to-machine payment models for data (x402 micropayments)

Autonomous agents also need an economic interface for data: they must be able to pay per request without manual invoicing, accounts, or long-lived sessions.

The x402 concept uses HTTP 402 Payment Required to communicate payment requirements directly in an API response (amount, currency, destination), enabling frictionless pay-per-use access for machine clients. It explicitly targets machine-to-machine payments and micropayments for API calls.

A typical x402 client flow is:

• make request

• receive a 402 with payment requirements in headers

• construct a signed payment payload

• retry the request with the payment attached

This model is architecturally compatible with agent tooling because it is:

• stateless (no sessions required)

• programmable (agents can implement it as a tool step)

• composable with Data Proxies (paid proxy services can aggregate and resell API capabilities)

In SEDA terms: x402-like micropayment flows are a clean way to let agents fund data acquisition dynamically, while Oracle Programs define how that acquired data is interpreted and delivered onchain.

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Security and determinism requirements for agent-driven oracle execution

If you want oracle infrastructure for autonomous agents to be a safe default, you need hard requirements—not best practices.

Determinism boundaries

• Oracle Program execution must be deterministic given the same inputs and policy.

• Probabilistic steps (LLM reasoning) must not be used to generate “consensus-critical” outputs directly.

• The interface must return structured outputs and context metadata so downstream checks are mechanical.

Reliability boundaries

Multi-agent systems introduce classical distributed failure modes (network partitions, retries, cascading errors). Orchestration guidance explicitly calls for timeouts, retries, graceful degradation, and validation at every handoff.

Oracle calls should be designed as idempotent request/response operations wherever possible, with explicit failure modes and deterministic fallbacks.

Least privilege + auditability

Agent systems should apply least privilege across tools and data access. Orchestration patterns emphasize secure networking, authentication, and audit trails because agents can inadvertently expand the blast radius of a single credential leak.

Data Proxies + Oracle Programs together give you:

• controlled access at the data boundary

• deterministic policy at the oracle boundary

• verifiable delivery at the onchain boundary

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When this model is unnecessary

You do not need oracle infrastructure for autonomous agents if:

• Your system is human-operated and data requests are infrequent and manual.

• A single public feed is sufficient and no policy is required beyond “read latest.”

• The workflow does not require deterministic replay (e.g., offchain-only assistants with no execution authority).

• You can tolerate non-deterministic outcomes because the agent’s output is advisory and not executable.

If the agent can’t cause state transitions, you can keep the “oracle” as an ordinary API integration. The moment it can trigger onchain actions, policy and determinism become infrastructure constraints.

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FAQ

1) How do AI agents onchain access external data safely?

They should access data through deterministic, verifiable interfaces that return both values and context, with explicit failure behavior and replayability. That’s what oracle infrastructure for autonomous agents provides.

2) Why are price feeds insufficient for agent decision systems?

Because agents require policy-executed results (validity windows, provenance, fallbacks, transformations), not just a number. They need context and constraints that can be checked mechanically.

3) What is an agent-executable Oracle Program?

An Oracle Program is a machine-invocable policy layer that defines how to source, transform, validate, and deliver external data onchain—so an agent can call it as a tool step in its workflow. In SEDA, Oracle Programs are where oracle logic lives (not in app glue).

4) How do machine-to-machine data requests differ from “calling an API”?

M2M requests are structured, programmatic, and typically integrated into an execution loop (retry, validation, checkpointing). They must produce deterministic outputs suitable for downstream automated action.

5) How can an AI agent pay for data dynamically?

Micropayment protocols like x402 use HTTP 402 responses to declare payment requirements and enable agents to pay per request programmatically, without accounts or sessions.

6) What are Data Proxies and why do agents need them?

Data Proxies are controlled intermediaries for private or authenticated APIs. They centralize key management, quotas, redaction, and auditability so agents don’t embed secrets directly in their execution environment.

7) How does determinism relate to “probabilistic” agents?

Agents can be probabilistic in planning, but their execution-critical data dependencies must be deterministic. A clean architecture separates probabilistic reasoning from deterministic oracle policy execution.

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Architectural close: why SEDA is oracle infrastructure for autonomous agents

Autonomous agents become economically and operationally meaningful only when they can execute against real external state—without turning every integration into bespoke, fragile glue.

SEDA is programmable oracle infrastructure for autonomous agents because it isolates and formalizes the parts that must be deterministic:

• data sourcing is separable from policy

• policy is encoded as Oracle Programs

• delivery is designed for onchain verification

That is the core of oracle infrastructure for autonomous agents: machine-executable data policy that makes agent-driven execution repeatable, auditable, and safe to compose.

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