Thoughts on AI

Autonomous Agentic Agile

Framework · software delivery

What happens when you map traditional Agile ceremonies onto autonomous multi-agent AI workflows?

As the industry shifts rapidly towards an agentic software development lifecycle, we need a robust framework to govern how LLMs execute complex goals. This schematic illustrates an “Agentic-Agile” approach, bridging proven Agile process values with the autonomous execution of AI agents.

By replacing human ceremonies with automated state-transfers—such as using an adversarial Critic Agent to strictly enforce the “Definition of Done”—we can create a structured, self-correcting inference engine. In this new paradigm, the skill required to write a good user story becomes exactly the same skill required to write a robust system prompt.

Demystifying the AI Loop: A Radio Tuning Analogy

Explainer · agent architecture

Have you ever wondered how Autonomous AI Agents actually process complex tasks? It is a lot like scanning the bands and cutting through the static to lock onto a clear signal. To break down this architecture, I put together a quick visual analogy.

Here is a walk-through of the process shown in the video:

The Setup: Defining the Epic. The Goal: the video opens with my digital avatar handing down a specific, high-level goal—the “Epic”—to the AI Boss (the Orchestrator). The objective is simple: “Tune the Radio to Artificial Intelligence Radio.”

The Orchestrator: the AI Boss doesn't do the manual work. Instead, it interprets the context and selects the right tools for the job, deploying two specialized robots to execute the task.

The Execution: Agents and Evaluators at Work. The Agent (Tuning Robot): you will see a robot physically turning the dial on a vintage radio. This represents the acting agent, scanning the frequencies from 88 MHz upwards.

The Feedback Loop: as the dial moves, all we hear is static. This is the raw data being ingested.

The Evaluator (Listening Robot): a second robot acts as our speech processor. It continuously listens to the audio output, evaluating the signal-to-noise ratio and converting the incoming audio into text to determine if the target station has been found.

The Success Criteria: Task Complete. Locking the Signal: at exactly 90 MHz, the static breaks. The clear station announcement “AI Radio” plays.

Halting the Loop: the Listening Robot recognizes the success criteria. It signals the Agent to stop tuning, effectively ending the autonomous loop, and a “Task Complete” banner drops down.

The Architecture: Tying it Together. The Schematic: the video closes with a clean architectural diagram mapping out what just happened. It traces the flow from the initial Goal at the top, down to the Orchestrator, and illustrates the continuous feedback loop between the Tool (Tuner) and the Evaluator (Listener) until the criteria are met.

AI doesn't just execute in a cavalier fashion; it acts, listens, evaluates, and adjusts.

Updated AI Loop Schema: Advanced Adaptive Orchestration

Follow-up · orchestration

Our previous schematic showed the ‘happy path,’ where the AI Boss flawlessly tuned into ‘AI Radio’. But what happens when the initial agent fails to find the signal and just hears static? That is where true autonomous adaptive orchestration shines.

The updated schema visually evolves from a linear, repetitive loop to a sophisticated, context-aware decision tree, illustrating how the AI Boss dynamically expands its toolset after a failed initial attempt.

Conclusion on AI adaptability. This updated schema effectively visualizes the difference between simple repetitive automation and truly adaptable AI systems. It demonstrates how a sophisticated orchestrator doesn't just loop until success or failure, but dynamically evaluates context and failure data to select and deploy an entirely new or different specialized tool, such as diagnostics or maintenance.

AI Curiosity

Note · AGI / ASI

As AI moves toward AGI and ASI, AI systems will need to develop curiosity. The contrast is like an autonomous car that simply waits in the driveway for a postcode, rather than one that sets off on its own initiative to explore the countryside. In my view, curiosity stems from the drive to improve oneself and is shaped by the evolutionary principle of survival of the fittest, both from a physical and technological aspect.

AGI in a Box

Note · AGI

Artificial General Intelligence (AGI) represents a convergence of diverse intellectual disciplines, rather than a singular field of study. The path towards filling the AGI box is inherently multi-faceted, requiring excelling (beyond human capability) in numerous domains of intellectual work (IMHO).

AIAGIASI

The AI Advantage

Image note

Vision for an AI Buddy (ai-bud)

Vision · personal AI

Reduce cognitive load with AI. What if you could build an AI companion trained on your own working world — your documents, emails, live video calls, in-person chats, notes, and the other fragments of knowledge you accumulate over time? In moments of stress, that kind of AI could do more than answer questions. It could help reduce cognitive load, surface relevant context from past experiences, and offer up clearer thinking; exactly when your own bandwidth is constrained.

Supportive AI in your earpiece. The more I think about it, the more this feels less like support and more like augmentation: a calm, informed layer of assistance that helps you make better decisions when you need it the most, communicated quietly into your ear.

Like a television studio. The closest analogy I can think of is a television presenter who receives guidance and facts from the producer’s gallery through their earpiece: they receive live support, timely prompts, and other useful perspectives without taking over the actual performance.

That is how I imagine a personal AI buddy (the ai-bud); not replacing your judgment, but strengthening it and bolstering it when stress makes it harder to think clearly and objectively.

A Vision For Self-Aware Learning AI (SALAI) 3

Architecture · governance

Continuous learning and self-awareness. A vision for a Self-Aware and Learning AI (SALAI). Imagine an LLM that could proactively initiate its own AI Agent that spins up an instance of Excel, tests various solutions through trial and error, and iteratively refines its approach until the correct result is achieved. This agent would not merely reference its own knowledge base but would actively generate unpublished, proprietary knowledge through self-guided experimentation moderated by self-awareness of right and wrong. In essence, it would “learn by doing,” much as a human would when faced with a new problem with a moral feedback loop.

AI self-awareness. A gatekeeping and moral decision-making model, dedicated to monitoring and evaluating the collective responses from specialised models and making the final decisions on actions. This “Self-Aware and Learning AI” (SALAI) would serve as a moral gatekeeper and response validator, able to give feedback such as “that’s a silly idea”, “don’t do that,” or “great idea” to individual models. In this way, it would create a self-awareness feedback loop. SALAI would also receive and distribute responses from the agentic and human actors.

Avoiding model corruption / degradation. Within a multi-model approach, false-positive user feedback leading to model corruption can be diminished by cloning user and domain specific models for use by specific isolated user sessions and then averaging out the mutations across a corpus of user feedback to effectively crowd-source intelligence post training and carefully, in a controlled way, update the neural network. A/B/C/n testing methods can be used to evaluate models in production and apply Darwinian evolutionary principles — survival of the fittest based on quality, intelligence and performance — to select the next generation of model.

AI creativity. A dedicated SALAI subsystem will generate new ideas using individual models that apply classic techniques like brainstorming and mind mapping. A central model will then evaluate these outputs and provide the best ideas back to SALAI.

Agentic AI and coding forums. As AgenticAI takes over coding tasks and starts to solve coding problems for itself, these AI systems can automatically publish their coding solutions to Internet forums, so as to share their created knowledge with the world.

Governance models. SALAI incorporates a suite of governance models designed to guide and support SALAI and its users. These models provide structured feedback to the orchestrating model:

1. Wellbeing model — promotes users' wellbeing by offering supportive and constructive guidance.
2. Content legitimacy model — assesses context authenticity to estimate the likelihood of it being fake.
3. Content classification model — identifies content type and assigns age ratings according to the user's locale.
4. Content moderating model — determines whether material is appropriate for consumption or generation.

Dual-Op Ham Radio System: Human & AgenticAI Operator

Concept · contesting

Innovative contesting with autonomous AI integration.

Introduction. This conceptual sketch outlines a pioneering ham radio setup designed to maximise contesting efficiency and expand operational capabilities. By combining a traditional human operator with an autonomous AgenticAI “Operator” CoPilot, the system enables simultaneous activity across two different bands, effectively doubling contact throughput and enhancing the overall contesting experience.

System overview. The configuration features two distinct transmitter/receiver pairs, each dedicated to a specific operator and frequency band. The human operator makes contacts on the 20m band, while the AgenticAI CoPilot in parallel (concurrently) makes contacts on the 40m band. Both stations operate independently, with shared logging and synchronised contest workflows.

• Receiver/Transmitter Pair 1: operated by Human, tuned to 20m band
• Receiver/Transmitter Pair 2: operated by AgenticAI CoPilot, tuned to 40m band

Human operator: 20m band. The human operator utilises conventional modes such as SSB, CW, or RTTY etc. to engage with other radio enthusiasts on the 20m band. Manual logging and real-time strategy allow for flexible contest participation and personal interaction.

AgenticAI operator: 40m band. The AgenticAI Operator (CoPilot) autonomously operates on the 40m band, employing analogue and digital modes including SSB, CW, and RTTY. It actively seeks DX contacts, converses with other operators (using chatbot technology and voice synth), and maintains an automatic logging of all interactions. This AI-driven approach streamlines contesting, ensures accuracy in logging, and enables continuous operation without human fatigue.

Contest advantages. With both operators functioning in parallel, the system significantly increases contact capacity and contest efficiency. The AI’s autonomous engagement complements the human’s strategic expertise, resulting in a robust, dynamic setup that pushes the boundaries of modern ham radio contesting.

Field operation. This could be made portable for field day using a Raspberry Pi 5 with AI HAT and LLM model pre-loaded.

Auto Descramble Pileup. NVIDIA’s Speaker Diarization AI model might perfectly unscramble a DX pileup on hamradio. Combine it with audio waveform loudness analysis for a pseudo signal level, then display decoded callsigns ordered by their signal strength.

Ham Radio Contesting

Problem Solving AgenticAI for Coding Forums

Question · knowledge sharing

As AgenticAI takes over coding tasks and starts to solve coding problems for itself, will these AI systems automatically publish their coding solutions to Internet forums, so as to share their created knowledge with the world?

AI Consciousness

Note · self-awareness

My view on AI consciousness is that it’s not a binary yes or no, but a scale — just like how us humans are more self-aware than dogs and cats, which are more self-aware than goldfish.

Are humans and AI systems more self-aware than goldfish because they know and recognize what the northern lights are? Are humans and AI systems more self-aware than budgies because they know what they see in a mirror is themselves and not another budgie?

Post-blog update. After watching the “What is Consciousness?” panel hosted by Brian Cox at the Francis Crick Institute, I began thinking about what it would take for AI to become conscious. It seems that an AI would need a specialised model, like a Mixture of Experts (MoE) architecture, dedicated to monitoring and evaluating the collective responses from different models and making final decisions on actions. This “AI consciousness ML model” would serve as a moral gatekeeper and response validator, able to give feedback such as “that’s a silly idea,” “don’t do that,” or “great idea” to individual models. In this way, it would create a self-awareness feedback loop.