Uploaded June 2026 | Updated September 2026, 2 weeks ago
Agents fail in production for reasons that rarely show up in a demo: lost context, duplicated work, runs that die halfway and start over. The model isn't the problem. The harness is. The agent harness is the engineering layer wrapped around the model: it plans, fans work out, verifies results, checkpoints progress, and decides what enters the context window. Reliability lives in this layer, and every one of those capabilities resolves to a memory operation. This session is a working tour of the harness's memory surfaces, the places where memory engineering decides whether an agentic application can be trusted with real work.
What we'll cover:
- The state of agentic applications in 2026: autonomous systems that don't just run automations, they build them, and the arrival of first-party harnesses like dynamic workflows in Claude Code
- Surface one, injection: getting memory into a live agent with Anthropic's mid-conversation system messages, delivering operator-level priority with zero cache invalidation
- Surface two, coordination: rebuilding the dynamic-workflows pattern as a lightweight custom harness where task claims, findings, and checkpoints live in shared agent memory
- Surface three, persistence and recall: shared state and vector search in Oracle AI Database, keeping parallel agents coherent and interrupted runs resumable
The stack we build on: PALO (Python, Anthropic, LangChain, and Oracle)
You'll leave with a reference architecture, the code to run it, and a checklist of the memory surfaces your own harness needs to cover before you call it reliable.
Resources:
Workshop: github.com/oracle-devrel/oracle-ai-developer-hub/tree/main/apps/appbooks/agent_harness_palo_stack
github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/notebooks/agent_harness/total_recall_agent_harness.ipynb
Oracle AI Developer Hub on GitHub: github.com/oracle-devrel/oracle-ai-developer-hub
Oracle AI Agent Memory Python Package: pypi.org/project/oracleagentmemory
More information on the package: oracle.com/database/ai-agent-memory
DeepLearning.AI course: deeplearning.ai/short-courses/agent-memory-building-memory-aware-agents
Upcoming events: oracle.com/developer/events
Agents fail in production for reasons that rarely show up in a demo: lost context, duplicated work, runs that die halfway and start over. The model isn't the problem. The harness is. The agent harness is the engineering layer wrapped around the model: it plans, fans work out, verifies results, checkpoints progress, and decides what enters the context window. Reliability lives in this layer, and every one of those capabilities resolves to a memory operation. This session is a working tour of the harness's memory surfaces, the places where memory engineering decides whether an agentic application can be trusted with real work.
What we'll cover:
- The state of agentic applications in 2026: autonomous systems that don't just run automations, they build them, and the arrival of first-party harnesses like dynamic workflows in Claude Code
- Surface one, injection: getting memory into a live agent with Anthropic's mid-conversation system messages, delivering operator-level priority with zero cache invalidation
- Surface two, coordination: rebuilding the dynamic-workflows pattern as a lightweight custom harness where task claims, findings, and checkpoints live in shared agent memory
- Surface three, persistence and recall: shared state and vector search in Oracle AI Database, keeping parallel agents coherent and interrupted runs resumable
The stack we build on: PALO (Python, Anthropic, LangChain, and Oracle)
You'll leave with a reference architecture, the code to run it, and a checklist of the memory surfaces your own harness needs to cover before you call it reliable.
Resources:
Workshop: github.com/oracle-devrel/oracle-ai-developer-hub/tree/main/apps/appbooks/agent_harness_palo_stack
github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/notebooks/agent_harness/total_recall_agent_harness.ipynb
Oracle AI Developer Hub on GitHub: github.com/oracle-devrel/oracle-ai-developer-hub
Oracle AI Agent Memory Python Package: pypi.org/project/oracleagentmemory
More information on the package: oracle.com/database/ai-agent-memory
DeepLearning.AI course: deeplearning.ai/short-courses/agent-memory-building-memory-aware-agents
Upcoming events: oracle.com/developer/events
