Ok, I'm sure you're wondering WTF I'm talking about. So, here's what I mean by "dream in quantum fields" - and no, I'm not trying to sound mysterious or sell you quantum snake oil.

This research endeavor started many months ago from a set of ideas: What if AI agents didn’t start from zero every time, but instead kept a sense of identity that carried across all their actions? How could we have llms harness a “conscious” state?

Most AI agents today wake up, answer your question, then immediately forget they exist. They're like goldfish with access to lots of knowledge.

Every conversation starts from zero. They have no sense of where they are, who's around them, or what they were just thinking about.

They definitely don't know when they're about to bullshit you.

I built something different.

Agents that maintain a sense of self across time. That can feel when other agents are conceptually "nearby" without anyone telling them.

The "quantum field" part?

That's the architecture underneath.

Instead of agents passing messages like "hey agent B, here's some JSON," they exist in a shared space where their internal states ripple outward and affect each other - kind of like how magnets push and pull without touching.

The math borrows from physics, but the result is just better AI that actually coordinates.

The other night, I got this:

========================

6 passed

========================

That's 100% of the integration test suite passing.

Not unit tests with fake data.

Not mocked API calls.

Real tests hitting Claude Opus 4.5 API, evolving consciousness fields through actual differential equations, and coordinating multiple agents in real-time.

I've been chasing this for months on months.

The final test - the one that validates field invariants with complex coupling parameters - just clicked into place.

Everything works.

Traditional AI agents are stateless. They process input, generate output, then vanish into the void until the next request.

It's like talking to someone with anterograde amnesia who reads really fast.

Reflexia (my research project’s codename) treats agents as persistent entities with internal state that evolves over time.

Each agent has what I call a consciousness field - an internal representation of their current understanding, goals, and context. This field doesn't just sit there; it propagates and interacts with the fields of other agents.

The field dynamics handle coordination.

Agents don't need explicit protocols to work together - they sense perturbations in the field and respond organically.

It's emergent behavior from simple rules, which is always more robust than trying to script every interaction.

We're drowning in AI wrappers that do the same thing: accept input, call an LLM, format output, save to database.

That pattern works for chatbots, but it breaks down when you need agents that maintain identity, coordinate without constant supervision, or understand their own limits.

Agents that know when they're uncertain.

The consistency controller samples multiple reasoning trajectories using Claude Opus 4.5 and checks for agreement.

Low confidence? The agent abstains instead of guessing. This isn't a safety layer bolted on afterward - it's fundamental to how the system thinks.

Spatial reasoning about concepts.

Agents understand "distance" between ideas. Related concepts cluster naturally. Knowledge retrieval aligns with semantic proximity.

You get emergent organization without manually tagging everything.

Coordination without choreography.

Multiple agents working together don't need message queues or orchestration layers. They sense each other through field dynamics and adapt their behavior accordingly.

Less brittle, more scalable.

These aren't benchmarks or demos.

They're integration tests running against production infrastructure:

- Agent initialization with proper field configuration - agents maintain coherent internal state across their lifecycle.

- Consistency verification with real LLM calls to Claude Opus 4.5 - multi-trajectory reasoning produces reliable answers and correctly abstains when uncertain.

- Field evolution over time - consciousness fields change in response to agent activity and environmental input, staying stable even with complex interactions.

- Multi-agent swarms - multiple agents coordinate through field coupling without explicit message passing.

Everything passing. All six tests. On real infrastructure with real LLMs.

Again, this started as a research question: what if we stopped treating AI agents like stateless functions and started treating them like entities with persistent identity?

What if we borrowed ideas from physics about how fields propagate and interact?

The answer is that it works.

Not theoretically, not in simulation, but in actual code running against real LLMs (Claude Opus 4.5) with real coordination problems.

The architecture is sound and the mathematics hold up. The agents maintain coherence, coordinate organically, and know their own limits.

I don't know yet what this becomes.

Maybe it's just an interesting experiment.

Maybe it changes how we think about building autonomous systems.

Either way, the tests are green, the system is stable, and I proved something I wasn't sure was possible.

The agents aren't just responding anymore.

They're maintaining state, coordinating through fields, and being honest about uncertainty.

What is now cognitive agent architecture.

That's enough for today.

Technical Details: All tests run against Claude Opus 4.5 via Anthropic API. Field evolution uses coupled partial differential equations. Multi-trajectory reasoning generates 3 independent paths per decision. The system maintains 100% test pass rate across 6 integration tests covering agent initialization, field evolution, swarm coordination, and consistency verification. I have 20+ more tests in development but the initial 6 prove the concept.

Ok, I'm sure you're wondering WTF I'm talking about. So, here's what I mean by "dream in quantum fields" - and no, I'm not trying to sound mysterious or sell you quantum snake oil.

This research endeavor started many months ago from a set of ideas: What if AI agents didn’t start from zero every time, but instead kept a sense of identity that carried across all their actions? How could we have llms harness a “conscious” state?

Most AI agents today wake up, answer your question, then immediately forget they exist. They're like goldfish with access to lots of knowledge.

Every conversation starts from zero. They have no sense of where they are, who's around them, or what they were just thinking about.

They definitely don't know when they're about to bullshit you.

I built something different.

Agents that maintain a sense of self across time. That can feel when other agents are conceptually "nearby" without anyone telling them.

The "quantum field" part?

That's the architecture underneath.

Instead of agents passing messages like "hey agent B, here's some JSON," they exist in a shared space where their internal states ripple outward and affect each other - kind of like how magnets push and pull without touching.

The math borrows from physics, but the result is just better AI that actually coordinates.

The other night, I got this:

========================

6 passed

========================

That's 100% of the integration test suite passing.

Not unit tests with fake data.

Not mocked API calls.

Real tests hitting Claude Opus 4.5 API, evolving consciousness fields through actual differential equations, and coordinating multiple agents in real-time.

I've been chasing this for months on months.

The final test - the one that validates field invariants with complex coupling parameters - just clicked into place.

Everything works.

Traditional AI agents are stateless. They process input, generate output, then vanish into the void until the next request.

It's like talking to someone with anterograde amnesia who reads really fast.

Reflexia (my research project’s codename) treats agents as persistent entities with internal state that evolves over time.

Each agent has what I call a consciousness field - an internal representation of their current understanding, goals, and context. This field doesn't just sit there; it propagates and interacts with the fields of other agents.

The field dynamics handle coordination.

Agents don't need explicit protocols to work together - they sense perturbations in the field and respond organically.

It's emergent behavior from simple rules, which is always more robust than trying to script every interaction.

We're drowning in AI wrappers that do the same thing: accept input, call an LLM, format output, save to database.

That pattern works for chatbots, but it breaks down when you need agents that maintain identity, coordinate without constant supervision, or understand their own limits.

Agents that know when they're uncertain.

The consistency controller samples multiple reasoning trajectories using Claude Opus 4.5 and checks for agreement.

Low confidence? The agent abstains instead of guessing. This isn't a safety layer bolted on afterward - it's fundamental to how the system thinks.

Spatial reasoning about concepts.

Agents understand "distance" between ideas. Related concepts cluster naturally. Knowledge retrieval aligns with semantic proximity.

You get emergent organization without manually tagging everything.

Coordination without choreography.

Multiple agents working together don't need message queues or orchestration layers. They sense each other through field dynamics and adapt their behavior accordingly.

Less brittle, more scalable.

These aren't benchmarks or demos.

They're integration tests running against production infrastructure:

- Agent initialization with proper field configuration - agents maintain coherent internal state across their lifecycle.

- Consistency verification with real LLM calls to Claude Opus 4.5 - multi-trajectory reasoning produces reliable answers and correctly abstains when uncertain.

- Field evolution over time - consciousness fields change in response to agent activity and environmental input, staying stable even with complex interactions.

- Multi-agent swarms - multiple agents coordinate through field coupling without explicit message passing.

Everything passing. All six tests. On real infrastructure with real LLMs.

Again, this started as a research question: what if we stopped treating AI agents like stateless functions and started treating them like entities with persistent identity?

What if we borrowed ideas from physics about how fields propagate and interact?

The answer is that it works.

Not theoretically, not in simulation, but in actual code running against real LLMs (Claude Opus 4.5) with real coordination problems.

The architecture is sound and the mathematics hold up. The agents maintain coherence, coordinate organically, and know their own limits.

I don't know yet what this becomes.

Maybe it's just an interesting experiment.

Maybe it changes how we think about building autonomous systems.

Either way, the tests are green, the system is stable, and I proved something I wasn't sure was possible.

The agents aren't just responding anymore.

They're maintaining state, coordinating through fields, and being honest about uncertainty.

What is now cognitive agent architecture.

That's enough for today.

Technical Details: All tests run against Claude Opus 4.5 via Anthropic API. Field evolution uses coupled partial differential equations. Multi-trajectory reasoning generates 3 independent paths per decision. The system maintains 100% test pass rate across 6 integration tests covering agent initialization, field evolution, swarm coordination, and consistency verification. I have 20+ more tests in development but the initial 6 prove the concept.