diff --git a/README.md b/README.md
index 9449cee..ce8949a 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,7 @@ We invite submissions of **5-minute talks** (including Q&A - nothing fancy - jus
 | [**Shashank Agarwal**](https://github.com/imshashank)<br>[noveum.ai](https://noveum.ai) · [𝕏](https://x.com/itsshashank) | AI agents are already silently failing in production — most enterprises just don’t realize it yet. […](https://github.com/milstan/sisw/issues/24) |
 | [**Alexandru Turcanu**](https://github.com/pondorasti)<br>[flaco.851.sh](https://flaco.851.sh) · [𝕏](https://x.com/pondorasti) | Making AI multiplayer.  Every company wants a self improving AI Agent like [River](https://x.com/tobi/status/2053121182044451016) or [Inspect](https://builders.ramp.com/post/why-we-built-our-background-agent), yet existing harnesses like OpenClaw quickly fall apart the moment you add them in Slack.  We’ll explore how we built an agent harness for teams and dive into the details that power it: - thread isolation with sandboxing - shared memory model - capabilities that go beyond MCPs |
 | [**Saai Arora**](https://github.com/Saai151)<br>[tryreplicas.com](https://tryreplicas.com) · [𝕏](https://x.com/saaiarora) | We're building replicas which is a cloud coding agent where you bring your own credentials for Claude Code or Codex. Customers like Mintlify and Composio use us to do everything from writing code to E2E testing via automations and integrations with Slack and Linear. Each agent works inside customizable VM environments, so work is parallelized and shareable across the team. Replicas can access sources like databases, Datadog or Notion, and return screenshots and videos as proof of work. |
+| [**Declan Johnston**](https://github.com/declanatinference)<br>[inference.net](https://inference.net) · [🔗](https://github.com/context-labs/HALO) | A general-purpose harness like Claude Code is the wrong tool for trace analysis. […](https://github.com/milstan/sisw/issues/31) |
 <!-- TALKS:END -->
 
 <!--
diff --git a/_data/talks.yml b/_data/talks.yml
index b4a9442..534d584 100644
--- a/_data/talks.yml
+++ b/_data/talks.yml
@@ -172,3 +172,26 @@
   submitter_github: Saai151
   submitted_at: '2026-05-21T18:33:53Z'
   issue: 28
+- first: Declan
+  last: Johnston
+  domain: inference.net
+  repo: https://github.com/context-labs/HALO
+  twitter: ''
+  summary: 'A general-purpose harness like Claude Code is the wrong tool for trace
+    analysis. This isn’t because the model isn’t smart, but because traces can get
+    extremely long, and you need a specialized toolkit in order to make observations
+    about systemic agentic behavior. HALO (Hierarchical Agent Loop Optimization) is
+    a methodology for building recursively self-improving agent harnesses using RLMs.
+
+
+    The core HALO loop is suprisingly simple:
+
+    1. Collect execution traces from your agent harness. HALO uses OpenTelemetry-compatible
+    tracing.
+
+    2. Feed traces into HALO-RLM engine.
+
+    3.'
+  submitter_github: declanatinference
+  submitted_at: '2026-05-21T20:17:59Z'
+  issue: 31