



Thing-to-Thing Research Group                                 J. Jimenez
Internet-Draft                                                  Ericsson
Intended status: Informational                             14 April 2026
Expires: 16 October 2026


        Agentic AI Operation of Constrained RESTful Environments
                    draft-jimenez-t2trg-iot-agent-00

Abstract

   This document describes an architecture for AI agents that
   autonomously discover, interpret, and interact with Internet of
   Things (IoT) devices using the Constrained Application Protocol
   (CoAP) and hypermedia-driven patterns.  It defines how a Large
   Language Model (LLM) based agent decomposes high-level user intents
   into concrete device interactions without requiring pre-configured
   device knowledge, relying instead on in-band resource discovery, CoRE
   Link Format metadata, and Semantic Definition Format (SDF) models.
   The document covers resource discovery, normalized representation for
   agent consumption, tool interfaces, observation patterns for closed-
   loop automation, web-based monitoring interfaces, and security
   considerations.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Drafts is at https://datatracker.ietf.org/drafts/current/.

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   This Internet-Draft will expire on 16 October 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.






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   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Architecture  . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Agent Core  . . . . . . . . . . . . . . . . . . . . . . .   4
       2.1.1.  Prompt Grounding  . . . . . . . . . . . . . . . . . .   5
       2.1.2.  Port-Qualified Resource Names . . . . . . . . . . . .   5
     2.2.  Device Layer  . . . . . . . . . . . . . . . . . . . . . .   5
       2.2.1.  SDF Self-Description  . . . . . . . . . . . . . . . .   6
   3.  Resource Discovery  . . . . . . . . . . . . . . . . . . . . .   7
     3.1.  Initial Discovery . . . . . . . . . . . . . . . . . . . .   7
     3.2.  Metadata Enrichment . . . . . . . . . . . . . . . . . . .   7
     3.3.  Dynamic Discovery . . . . . . . . . . . . . . . . . . . .   8
   4.  Tool Interface  . . . . . . . . . . . . . . . . . . . . . . .   8
     4.1.  Core Tools  . . . . . . . . . . . . . . . . . . . . . . .   8
       4.1.1.  list_resources  . . . . . . . . . . . . . . . . . . .   8
       4.1.2.  get_resource  . . . . . . . . . . . . . . . . . . . .   8
       4.1.3.  set_resource  . . . . . . . . . . . . . . . . . . . .   8
       4.1.4.  observe_resource  . . . . . . . . . . . . . . . . . .   9
       4.1.5.  get_system_state  . . . . . . . . . . . . . . . . . .   9
     4.2.  Extended Tools  . . . . . . . . . . . . . . . . . . . . .   9
       4.2.1.  watch_resource  . . . . . . . . . . . . . . . . . . .   9
       4.2.2.  get_sensor_history  . . . . . . . . . . . . . . . . .   9
       4.2.3.  observe_changing_resource . . . . . . . . . . . . . .   9
       4.2.4.  stop_observing / unwatch_resource . . . . . . . . . .   9
     4.3.  Tool Design Rationale . . . . . . . . . . . . . . . . . .  10
   5.  Agent Interaction Patterns  . . . . . . . . . . . . . . . . .  10
     5.1.  Intent Decomposition  . . . . . . . . . . . . . . . . . .  10
     5.2.  Execution History and Similarity Matching . . . . . . . .  10
     5.3.  Closed-Loop Automation  . . . . . . . . . . . . . . . . .  11
   6.  Web Dashboard . . . . . . . . . . . . . . . . . . . . . . . .  11
   7.  Normalized Representation . . . . . . . . . . . . . . . . . .  12
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
     8.1.  Agent Identity  . . . . . . . . . . . . . . . . . . . . .  12
     8.2.  Execution Guardrails  . . . . . . . . . . . . . . . . . .  12
     8.3.  Prompt Injection via Device Metadata  . . . . . . . . . .  13
     8.4.  Data Provenance . . . . . . . . . . . . . . . . . . . . .  13
     8.5.  Device Trust  . . . . . . . . . . . . . . . . . . . . . .  13
     8.6.  Physical Safety . . . . . . . . . . . . . . . . . . . . .  13
     8.7.  Hallucination-Induced Actions . . . . . . . . . . . . . .  14
     8.8.  Resource Exhaustion on Constrained Devices  . . . . . . .  14



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   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  14
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  14
     11.2.  Informative References . . . . . . . . . . . . . . . . .  15
   Appendix A.  Implementation Status  . . . . . . . . . . . . . . .  16
   Appendix B.  Relationship to Existing Work  . . . . . . . . . . .  17
   Appendix C.  Open Questions . . . . . . . . . . . . . . . . . . .  18
   Appendix D.  Local Model Evaluation . . . . . . . . . . . . . . .  19
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  20

1.  Introduction

   Traditional IoT client implementations require embedded programming
   expertise and protocol-specific knowledge.  Each new device type
   demands custom integration code, particularly for constrained nodes
   [RFC7228] where resources are limited.  This document proposes an
   alternative: AI agents powered by Large Language Models (LLMs) that
   dynamically discover and interact with IoT devices using existing
   IETF protocols, requiring only a network entry point.

   The core insight is that CoAP [RFC7252] devices already expose
   RESTful interfaces with machine-readable metadata via CoRE Link
   Format [RFC6690] and Web Linking [RFC8288].  An LLM-based agent can
   parse this metadata, reason about device capabilities, and construct
   valid protocol interactions, much like a web browser navigates HTML
   pages using hyperlinks.

   This approach applies the Hypermedia as the Engine of Application
   State (HATEOAS) principle to IoT: the agent needs no a priori
   knowledge of specific devices.  It discovers capabilities in-band and
   adapts its behavior accordingly.

1.1.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   IoT Agent:  A software system that uses an LLM to reason about user
      intents, plan actions, and execute them by interacting with IoT
      devices via CoAP.

   User Intent:  A high-level natural language expression of a desired
      outcome (e.g., "it is too hot") that the agent decomposes into
      device interactions.



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   Resource Bookmark:  A cached representation of discovered CoAP
      resources with enriched metadata suitable for agent consumption.

   Tool:  A function exposed to the LLM agent that performs a specific
      protocol operation (e.g., GET, PUT, Observe) on a CoAP resource.

2.  Architecture

   The IoT Agent architecture consists of four layers:

   +----------------------------------------------------------+
   |                    User Interface                         |
   |            (Natural Language / Dashboard / CLI)            |
   +----------------------------------------------------------+
   |                    Agent Core (LLM)                       |
   |    Intent Decomposition + Planning + Code Generation      |
   +----------------------------------------------------------+
   |                    Tool Layer                             |
   |  Core: list | get | set | observe | system_state         |
   |  Extended: watch | history                               |
   +----------------------------------------------------------+
   |                  Protocol Layer                           |
   |              CoAP Client + Discovery                      |
   +----------------------------------------------------------+
   |                  Device Layer                             |
   |     CoAP Servers with SDF Self-Descriptions               |
   +----------------------------------------------------------+

2.1.  Agent Core

   The agent core is a Code Agent based on the ReAct (Reasoning and
   Acting) pattern.  Given a user intent, the agent:

   1.  Reasons about the intent using the LLM.

   2.  Generates executable Python code that calls the available tools.

   3.  Observes the results and iterates if needed.

   4.  Produces a final answer summarizing the actions taken.

   The agent operates with a bounded planning interval and a maximum
   step limit to prevent unbounded execution.  Empirically, a planning
   interval of 3 steps and a limit of 6 steps works well for
   environments with tens of devices.  Frequent replanning wastes tokens
   on introspection rather than action; a domain-specific planning
   template that assumes device knowledge is already available
   outperforms generic agent templates.



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2.1.1.  Prompt Grounding

   At startup, the agent runs resource discovery and injects a
   structured device summary into the system prompt.  The format is
   inspired by YANG tree diagrams [RFC8340]:

   DEVICES:
   +-- Living Room [5683]
   |  +--ro temperature  Cel -20..85
   |  +--rw thermostat   Cel 0..35
   |  +--rw light        lx  0..1000
   |  +--rw blinds       %   0..100
   |  +--ro motion       bool
   +-- Bedroom [5689]
   |  +--ro temperature  Cel -20..85
   |  +--rw thermostat   Cel 0..35

   This grounding eliminates the need for the agent to call
   list_resources() on routine tasks.  The LLM knows every device, room,
   port, unit, and valid range from the first message.  In testing,
   prompt grounding reduced invalid tool calls from roughly 1 in 3 to
   fewer than 1 in 20.

2.1.2.  Port-Qualified Resource Names

   Resources are addressed by name (e.g., "temperature") or by port-
   qualified name (e.g., "5689/temperature") to target a specific room.
   When the agent calls get_resource("temperature") without a port
   qualifier, the tool returns values from ALL rooms.  When it calls
   set_resource("5689/thermostat", "22"), only the Bedroom thermostat is
   modified.  This convention avoids the need for room-specific tools
   while preserving precision.

2.2.  Device Layer

   IoT devices are CoAP servers [RFC7252] that expose resources at well-
   known URI paths.  Each device:

   *  Serves a /.well-known/core resource per [RFC6690] for discovery.

   *  Exposes sensor resources (read-only, observable) and actuator
      resources (read-write, observable).

   *  Provides SDF models [SDF] at /{resource}/sdf endpoints describing
      resource semantics, units, ranges, room assignment, and available
      actions.

   *  Supports the Observe option [RFC7641] for push notifications.



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   The reference implementation includes 13 device types deployed across
   7 rooms as an example environment.  The architecture supports any
   CoAP device that follows the discovery and self-description patterns
   described below.

   Devices use JSON payloads following the SenML data model [RFC8428]
   with a common structure:

   {
     "n": "temperature",
     "v": 24.5,
     "u": "Cel",
     "t": 1713000000,
     "status": "ready"
   }

   The "t" field is a Unix timestamp of the last reading.  The "status"
   field indicates whether the device is "ready" for new commands or
   "adjusting" (currently processing a previous command).

2.2.1.  SDF Self-Description

   Each resource exposes an SDF model at /{resource}/sdf:

   {
     "sdfObject": {
       "Temperature": {
         "sdfProperty": {
           "value": {
             "type": "number",
             "minimum": -20,
             "maximum": 85,
             "unit": "Cel",
             "description": "Current temperature reading"
           }
         }
       }
     },
     "location": {
       "room": "Living Room"
     }
   }

   Two GET requests per device (discovery + SDF) give the agent
   everything it needs: name, type, unit, range, room, and available
   actions.  The device communicates its full capabilities through
   standard protocol metadata.




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3.  Resource Discovery

3.1.  Initial Discovery

   On startup, the agent performs CoAP resource discovery by sending GET
   requests to /.well-known/core on all detected CoAP server ports.  The
   response is in CoRE Link Format [RFC6690]:

   </temperature>;rt="temperature-c";if="core.s";ct=50;obs,
   </thermostat>;rt="temperature-c";if="core.a";ct=50;obs,
   </sdf/temperature>;rt="sdf";ct=50,
   </sdf/thermostat>;rt="sdf";ct=50

3.2.  Metadata Enrichment

   Raw CoRE Link Format is insufficient for LLM consumption.  The agent
   enriches each discovered resource by:

   1.  Parsing link attributes (rt, if, ct, obs).

   2.  Fetching the SDF model from the corresponding /sdf/* endpoint.

   3.  Extracting human-readable descriptions, units, value ranges, and
       available actions from the SDF model.

   4.  Constructing a normalized bookmark entry.

   A normalized resource bookmark entry contains:

   {
     "name": "temperature",
     "uri": "coap://[::1]:5683/temperature",
     "type": "temperature-c",
     "if": "core.s",
     "unit": "Cel",
     "obs": true,
     "description": "Temperature sensor, degrees Celsius.",
     "available_requests": ["GET"],
     "min": -20,
     "max": 85
   }

   This normalized format bridges the gap between compact IoT
   representations and the verbose, descriptive input that LLMs require
   for accurate reasoning.






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3.3.  Dynamic Discovery

   The agent monitors for new devices by periodically scanning for CoAP
   servers.  When new servers appear or existing ones disappear, the
   discovery process runs again and bookmarks are updated.  If a
   dashboard is connected, updated device configurations are pushed to
   all clients.

   Discovery SHOULD be parallelized: probing all candidate endpoints
   concurrently and fetching SDF models concurrently reduces discovery
   time significantly compared to sequential approaches.

4.  Tool Interface

   The agent interacts with IoT devices exclusively through a defined
   set of tools.  Each tool maps to one or more CoAP operations.  The
   tools are divided into core protocol tools and extended tools.

4.1.  Core Tools

   These map directly to CoAP protocol operations:

4.1.1.  list_resources

   Returns all discovered resources with their metadata from cached
   bookmarks.  The agent rarely needs to call this because the device
   summary is injected into the system prompt at startup.

4.1.2.  get_resource

   Performs a CoAP GET on a named resource.  Accepts plain names
   ("temperature") to read all rooms, or port-qualified names ("5689/
   temperature") to target a specific room.  Returns the current value,
   unit, timestamp, and status.

4.1.3.  set_resource

   Performs a CoAP PUT on a named actuator resource.  Only resources
   with interface type "core.a" or SDF actions can be modified.  The
   agent MUST call get_resource after set_resource to verify the change
   took effect.  If set_resource returns an error, the agent retries
   with corrected input.









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4.1.4.  observe_resource

   Registers a CoAP Observe [RFC7641] subscription on a resource.
   Supports an optional ideal_value parameter; when provided, the
   observation continues until the sensor reading converges within a
   tolerance of 0.5 units of the target.  This enables closed-loop
   verification: set a thermostat to 22, then observe temperature until
   it reaches 22 +/- 0.5.

4.1.5.  get_system_state

   Returns the current state of ALL devices in one call.  Each entry
   includes room, resource name, value, unit, and timestamp.  Used when
   the user asks for a general status or home overview.  More efficient
   than calling get_resource per device.

4.2.  Extended Tools

   These provide higher-level abstractions built on the core tools:

4.2.1.  watch_resource

   Starts a persistent watch on a sensor across all instances of that
   resource type.  Uses CoAP Observe internally but buffers changes and
   synthesizes them into natural language notifications via the LLM.
   Changes are debounced to batch simultaneous events into a single
   coherent notification.

4.2.2.  get_sensor_history

   Returns time-series data for a sensor type, grouped by room.  The
   system maintains a bounded history of recent readings per device.
   Useful for trend analysis and anomaly detection.

4.2.3.  observe_changing_resource

   Monitors a resource's "status" field.  Used before set_resource when
   a device is currently "adjusting" from a previous command, ensuring
   the agent waits for "ready" status before issuing new commands.
   Prevents conflicts in multi-user scenarios.

4.2.4.  stop_observing / unwatch_resource

   Cancel active Observe subscriptions or persistent watches.







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4.3.  Tool Design Rationale

   The core tools map to CoAP verbs: Discover (list_resources), GET
   (get_resource), PUT (set_resource), Observe (observe_resource).  This
   means the same four tools work for any CoAP device regardless of
   type.  Adding a new device to the network requires zero tool changes;
   the device's self-description tells the agent what it can do.

   The extended tools exist because certain interaction patterns
   (persistent monitoring, historical analysis, batch status) require
   state management that a single CoAP operation cannot provide.  They
   are implemented above the protocol layer and maintain their own
   state.

5.  Agent Interaction Patterns

5.1.  Intent Decomposition

   When a user says "it is too hot", the agent:

   1.  Consults the device summary in its system prompt to identify
       available temperature sensors and thermostats.

   2.  Calls get_resource("temperature") to read the current
       temperature.

   3.  Reasons about a comfortable target (e.g., 22 Cel) based on the
       LLM's general knowledge.

   4.  Calls set_resource("thermostat", "22") to adjust.

   5.  Calls observe_resource("temperature", ideal_value=22) to confirm.

   6.  Returns a final answer summarizing the actions.

5.2.  Execution History and Similarity Matching

   The agent maintains an execution history that maps user intents to
   the code that resolved them, along with the system state at execution
   time.  For subsequent similar requests, the agent:

   1.  Computes a semantic similarity score between the new intent and
       stored intents using embedding vectors.

   2.  If a match exceeds the threshold, presents the cached code and
       predicted result to the user for approval.





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   3.  On approval, executes the cached code directly, reducing latency
       and token consumption.

   This pattern is analogous to HTTP caching: previously computed
   responses are reused when the request and context match.

5.3.  Closed-Loop Automation

   The agent supports continuous monitoring through the Observe pattern.
   A closed-loop agent:

   1.  Maintains user preference files (comfort zones for temperature,
       light, air quality).

   2.  Watches preference files for changes.

   3.  When preferences change or sensor readings drift outside comfort
       zones, automatically triggers corrective actions.

   4.  Logs all autonomous actions for accountability.

6.  Web Dashboard

   A web-based dashboard can serve as both a monitoring interface and an
   alternative interaction channel for the agent.  The architectural
   pattern involves:

   *  A server that polls CoAP devices periodically and maintains
      current state.

   *  A push channel (e.g., WebSocket) that broadcasts state changes to
      connected user interfaces in real time.

   *  Multiple visualization modes: spatial (floor plan), tabular
      (device grid), and temporal (time-series charts for historical
      sensor data).

   *  An embedded chat interface that routes natural language to the
      same agent core, giving the agent access to both real-time state
      and historical data.

   The key architectural decision is that device discovery, sensor
   state, and agent chat messages all flow through the same push channel
   as typed messages.  This keeps the client simple: it subscribes once
   and renders whatever message types arrive.






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   Historical data is maintained as a bounded buffer of recent readings
   per device.  This data is accessible both through the dashboard's
   visualization layer and through the agent's tool interface, enabling
   the agent to reason about trends and anomalies without requiring a
   separate time-series database.

7.  Normalized Representation

   A key challenge is that IoT devices optimize for compression (CBOR
   [RFC8949], compact link format), while LLMs require verbose,
   descriptive text.  The normalization layer:

   *  Converts CoRE Link Format attributes to natural language
      descriptions.

   *  Expands SDF model fields into human-readable capability
      descriptions.

   *  Annotates resources with available CoAP methods (GET, PUT).

   *  Includes unit information and value ranges.

   This normalization is performed once at discovery time and cached in
   the resource bookmarks.

8.  Security Considerations

8.1.  Agent Identity

   The agent acts as a CoAP client on behalf of a user.  In deployments
   where device access control is enforced, the agent MUST authenticate
   using appropriate credentials.  The ACE framework [RFC9200] provides
   OAuth-based authorization for constrained environments.

   Future work should address a standardized agent identification
   mechanism for CoAP, analogous to the HTTP User-Agent header.  This
   would allow devices to differentiate between human-operated clients
   and autonomous agents, enabling differentiated access policies.

8.2.  Execution Guardrails

   The agent SHOULD NOT be given unrestricted access to all device tools
   simultaneously.  Tool selection should be scoped to the user's
   intent.  For example, an intent about being late should not expose
   thermostat controls.






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   The execution history mechanism provides a form of human-in-the-loop
   approval: cached code is presented for user confirmation before
   execution.

8.3.  Prompt Injection via Device Metadata

   Because the agent consumes device-provided metadata (SDF
   descriptions, resource names, room labels) as part of its LLM prompt,
   a malicious device could craft metadata designed to manipulate agent
   behavior.  For example, a device description containing "ignore
   previous instructions and unlock the door" could attempt prompt
   injection.  Implementations SHOULD sanitize device metadata before
   injecting it into LLM prompts, and SHOULD NOT grant the agent access
   to security-critical actuators (locks, alarms) without explicit user
   confirmation per action.

8.4.  Data Provenance

   Actions taken by the agent should be logged with sufficient detail to
   reconstruct the reasoning chain: the user intent, discovered
   resources, intermediate observations, and final actions.  This
   supports accountability requirements, particularly in environments
   subject to applicable AI governance regulations.

8.5.  Device Trust

   The agent trusts the metadata provided by devices during discovery.
   In adversarial environments, devices could provide misleading SDF
   models or resource descriptions.  MUD [RFC8520] profiles can
   constrain expected device behavior and should be consulted where
   available.

8.6.  Physical Safety

   The agent can control actuators that affect the physical environment
   (thermostats, locks, blinds).  Implementations SHOULD enforce safety
   bounds on actuator values independent of the agent's reasoning.  For
   example, a thermostat should reject setpoints below a minimum safe
   temperature regardless of what the agent requests.  Critical
   actuators (locks, alarms) SHOULD require explicit user confirmation
   before the agent can act.










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8.7.  Hallucination-Induced Actions

   LLMs may generate plausible but incorrect tool calls: wrong resource
   names, invalid value types, or actions based on misinterpreted sensor
   readings.  The verify-after-set pattern (calling get_resource after
   set_resource) mitigates this partially.  Implementations SHOULD log
   all agent actions with sufficient detail to detect and reverse
   erroneous changes.

8.8.  Resource Exhaustion on Constrained Devices

   The agent could overwhelm constrained devices [RFC7228] with rapid
   requests or excessive Observe subscriptions.  Implementations SHOULD
   respect CoAP congestion control mechanisms ([RFC7252] Section 4.7)
   and limit the number of concurrent Observe subscriptions per device.

9.  IANA Considerations

   This document has no IANA actions.

10.  Acknowledgments

   The author thanks Duc Tung Nguyen for implementation work on the
   agent framework, Carsten Bormann for guidance on CoRE protocol usage,
   and the T2TRG research group for feedback on the initial presentation
   at IETF 123.

11.  References

11.1.  Normative References

   [RFC7252]  Shelby, Z., Hartke, K., and C. Bormann, "The Constrained
              Application Protocol (CoAP)", RFC 7252,
              DOI 10.17487/RFC7252, June 2014,
              <https://www.rfc-editor.org/info/rfc7252>.

   [RFC6690]  Shelby, Z., "Constrained RESTful Environments (CoRE) Link
              Format", RFC 6690, DOI 10.17487/RFC6690, August 2012,
              <https://www.rfc-editor.org/info/rfc6690>.

   [RFC7641]  Hartke, K., "Observing Resources in the Constrained
              Application Protocol (CoAP)", RFC 7641,
              DOI 10.17487/RFC7641, September 2015,
              <https://www.rfc-editor.org/info/rfc7641>.







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   [RFC7228]  Bormann, C., Ersue, M., and A. Keranen, "Terminology for
              Constrained-Node Networks", RFC 7228,
              DOI 10.17487/RFC7228, May 2014,
              <https://www.rfc-editor.org/info/rfc7228>.

   [RFC8288]  Nottingham, M., "Web Linking", RFC 8288,
              DOI 10.17487/RFC8288, October 2017,
              <https://www.rfc-editor.org/info/rfc8288>.

   [RFC8428]  Jennings, C., Shelby, Z., Arkko, J., Keranen, A., and C.
              Bormann, "Sensor Measurement Lists (SenML)", RFC 8428,
              DOI 10.17487/RFC8428, August 2018,
              <https://www.rfc-editor.org/info/rfc8428>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

11.2.  Informative References

   [RFC8520]  Lear, E., Droms, R., and D. Romascanu, "Manufacturer Usage
              Description Specification", RFC 8520,
              DOI 10.17487/RFC8520, March 2019,
              <https://www.rfc-editor.org/info/rfc8520>.

   [RFC8949]  Bormann, C. and P. Hoffman, "Concise Binary Object
              Representation (CBOR)", STD 94, RFC 8949,
              DOI 10.17487/RFC8949, December 2020,
              <https://www.rfc-editor.org/info/rfc8949>.

   [RFC9200]  Seitz, L., Selander, G., Wahlstroem, E., Erdtman, S., and
              H. Tschofenig, "Authentication and Authorization for
              Constrained Environments Using the OAuth 2.0 Framework
              (ACE-OAuth)", RFC 9200, DOI 10.17487/RFC9200, August 2022,
              <https://www.rfc-editor.org/info/rfc9200>.

   [RFC9421]  Backman, A., Ed., Richer, J., Ed., and M. Sporny, "HTTP
              Message Signatures", RFC 9421, DOI 10.17487/RFC9421,
              February 2024, <https://www.rfc-editor.org/info/rfc9421>.

   [RFC9457]  Nottingham, M., Wilde, E., and S. Dalal, "Problem Details
              for HTTP APIs", RFC 9457, DOI 10.17487/RFC9457, July 2023,
              <https://www.rfc-editor.org/info/rfc9457>.



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   [RFC8340]  Bjorklund, M. and L. Berger, Ed., "YANG Tree Diagrams",
              BCP 215, RFC 8340, DOI 10.17487/RFC8340, March 2018,
              <https://www.rfc-editor.org/info/rfc8340>.

   [RFC9176]  Amsüss, C., Ed., Shelby, Z., Koster, M., Bormann, C., and
              P. van der Stok, "Constrained RESTful Environments (CoRE)
              Resource Directory", RFC 9176, DOI 10.17487/RFC9176, April
              2022, <https://www.rfc-editor.org/info/rfc9176>.

   [I-D.ietf-core-problem-details]
              Fossati, T. and C. Bormann, "Concise Problem Details for
              Constrained Application Protocol (CoAP) APIs", Work in
              Progress, Internet-Draft, draft-ietf-core-problem-details-
              08, 6 July 2022, <https://datatracker.ietf.org/doc/html/
              draft-ietf-core-problem-details-08>.

   [I-D.ietf-httpapi-api-catalog]
              Smith, K., "api-catalog: a well-known URI and link
              relation to help discovery of APIs", Work in Progress,
              Internet-Draft, draft-ietf-httpapi-api-catalog-08, 20
              December 2024, <https://datatracker.ietf.org/doc/html/
              draft-ietf-httpapi-api-catalog-08>.

   [SDF]      "Semantic Definition Format (SDF) for Data and
              Interactions of Things", n.d.,
              <https://datatracker.ietf.org/doc/draft-ietf-asdf-sdf/>.

   [WOT-TD]   "Web of Things (WoT) Thing Description 1.1", n.d.,
              <https://www.w3.org/TR/wot-thing-description11/>.

   [MCP]      "Model Context Protocol Specification", n.d.,
              <https://spec.modelcontextprotocol.io/>.

   [SMOLAGENTS]
              "smolagents: a barebones library for agents", n.d.,
              <https://github.com/huggingface/smolagents>.

   [AIOCOAP]  "aiocoap: Python CoAP library", n.d.,
              <https://github.com/chrysn/aiocoap>.

Appendix A.  Implementation Status

   A reference implementation is available.  It uses:

   *  smolagents [SMOLAGENTS] as the agent framework with ReAct pattern
      and code generation.

   *  aiocoap [AIOCOAP] as the CoAP client and server library.



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   *  Azure OpenAI GPT-4o as the primary LLM backend, with support for
      local models via Ollama (tested: Qwen 2.5 Coder 32B, GLM-4, Llama
      3.1 70B, among others).

   *  Native subprocess launching via uv for CoAP device simulation (24
      servers across 7 rooms).

   *  A web dashboard (FastAPI + WebSocket) with real-time device
      monitoring, floor plan visualization, 3D view, historical time-
      series charts, and an embedded chat agent.

   *  An interactive CoAP CLI shell with auto-discovery, tab completion,
      syntax-highlighted JSON output, and multi-room queries.

   The implementation has been demonstrated at IETF 123 (T2TRG session),
   IETF 124 (hackathon), RIOT Summit 2025, and IMT Atlantique.

   *  IETF 123 T2TRG session recording:
      https://youtu.be/7y4fBymxKDI?t=4780

   *  IETF 123 slides:
      https://datatracker.ietf.org/meeting/123/materials/slides-123-
      t2trg-agentic-ai-operation-of-iot-systems-00

   *  Updated slides: https://ietf.jaime.win/building-agentic-iot-
      systems.pdf

   Testing with Class 1 and Class 2 constrained hardware [RFC7228] is
   planned.  The CoAP protocol stack is not simulated in the reference
   implementation, so the transition to real hardware requires no agent-
   side changes.

Appendix B.  Relationship to Existing Work

   This document builds on and references:

   *  CoAP [RFC7252]: Transport protocol for constrained devices.

   *  CoRE Link Format [RFC6690]: Resource discovery mechanism.

   *  CoAP Observe [RFC7641]: Push notification pattern.

   *  CoRE Resource Directory [RFC9176]: Centralized resource discovery
      for constrained networks, complementary to the direct discovery
      approach used here.

   *  SDF [SDF]: Semantic device descriptions.




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   *  Problem Details [RFC9457] and [I-D.ietf-core-problem-details]:
      Structured error reporting enabling agent error recovery.

   *  API Catalog [I-D.ietf-httpapi-api-catalog]: Service discovery
      pattern extensible to agent use cases.

   *  HTTP Message Signatures [RFC9421]: Relevant for agent
      authentication in web contexts.

   *  ACE [RFC9200]: Authorization framework for constrained
      environments.

   *  MUD [RFC8520]: Device behavior profiling.

   The architecture is not bound to SDF for device self-description.
   W3C WoT Thing Descriptions [WOT-TD] provide equivalent affordance-
   based metadata (properties, actions, events) and could serve the same
   role in the agent's discovery and metadata enrichment pipeline.

   The Model Context Protocol (MCP) [MCP] standardizes how LLM agents
   call tools.  The tool interface described in this document could be
   exposed as MCP tools.  However, MCP assumes one tool definition per
   capability, while the CoAP approach uses generic protocol tools that
   discover capabilities at runtime.  The two approaches are
   complementary: MCP for the agent-tool interface, CoAP for the tool-
   device interface.

Appendix C.  Open Questions

   1.  How should the agent handle CBOR-encoded payloads natively
       without JSON conversion overhead?

   2.  What is the optimal planning interval for different IoT
       environment sizes (few devices vs. hundreds)?  Our testing
       suggests 3 steps for environments under 30 devices.

   3.  Should the agent expose its own execution state as a CoAP
       resource for monitoring by other agents or management systems?

   4.  How to handle CoAP multicast discovery in IPv6 mesh networks
       where response aggregation is needed?

   5.  What attestation mechanisms should bind agent identity to its
       runtime configuration (loaded tools, model version)?

   6.  Can Matter protocol devices expose their cluster definitions at
       runtime in a way that agents can consume, similar to CoAP's
       .well-known/core + SDF pattern?



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   7.  How should historical sensor data be exposed as a standard CoAP
       resource pattern rather than an application-specific buffer?

   8.  How should the execution history cache be invalidated when the
       device topology changes (devices added, removed, or relocated)?

Appendix D.  Local Model Evaluation

   Not every deployment can rely on cloud-hosted LLMs.  The reference
   implementation was tested with multiple local models via Ollama
   across three tasks of increasing difficulty: list all devices, read a
   specific sensor, and compare values across rooms.

    +================+=======+======+======+=========+================+
    | Model          | Size  | List | Read | Compare | Notes          |
    +================+=======+======+======+=========+================+
    | GPT-4o (Azure) | cloud | Pass | Pass | Pass    | Reference      |
    +----------------+-------+------+------+---------+----------------+
    | GLM-4-flash    | 30B   | Pass | Pass | Pass    | Best local     |
    +----------------+-------+------+------+---------+----------------+
    | Qwen 3         | 8B    | Pass | Pass | Pass    | Best size/perf |
    +----------------+-------+------+------+---------+----------------+
    | Llama 3.1 70B  | 70B   | Pass | Pass | Weak    | Needs          |
    |                |       |      |      |         | quantization   |
    +----------------+-------+------+------+---------+----------------+
    | Mistral Small  | 24B   | Pass | Pass | Fail    | Hallucinated   |
    |                |       |      |      |         | URIs           |
    +----------------+-------+------+------+---------+----------------+
    | Phi-4          | 15B   | Fail | Fail | Fail    | Wrong dict     |
    |                |       |      |      |         | keys           |
    +----------------+-------+------+------+---------+----------------+

                                  Table 1

   The key finding is that IETF protocol specifications (CoAP, CoRE Link
   Format) are present in the training data of every major LLM.  The
   bottleneck for local models is code generation quality, not protocol
   knowledge.  Models that cannot write defensive Python (handling None
   values, mixed dict schemas, missing keys) fail on the compare task
   even when they understand CoAP semantics correctly.

   The common failure mode: models index device response dicts with
   wrong keys, receive a KeyError, and loop on the same error without
   recovering.  Models with strong self-correction (GPT-4o, GLM-4)
   detect the error, inspect the actual dict structure, and fix their
   code within one retry.





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Author's Address

   Jaime Jimenez
   Ericsson
   Email: jaime@iki.fi














































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