Deep in the Australian outback, termite mounds rise like ancient skyscrapers from the red earth. These remarkable structures, some reaching heights of six meters, aren’t just architectural marvels – they’re a testament to one of nature’s most sophisticated engineering principles: stigmergy. And while I promise this isn’t just another piece where we torture natural analogies until they confess to being relevant to technology (though there will be some of that), there’s something profound in how these tiny architects solve massive coordination problems.
The humble termite, with a brain smaller than a pinhead, participates in the construction of climate-controlled structures complete with ventilation systems, fungal gardens, and nurseries. Yet no single termite understands the blueprint. There is no master architect, no central planning committee, no weekly status meetings (and thankfully, no change management paperwork). What they have instead is something we’ve spent the last year building into Agentforce: structured patterns of interaction that create coordinated behavior from simple components.
The Infrastructure Labyrinth
Australia’s infrastructure development landscape bears an uncanny resemblance to our termite friends’ challenge – though with considerably more complexity and rather more stakeholders to consider. The scale is massive, the processes intricate, and the need for coordination paramount.
According to recent analysis by Herbert Smith Freehills and the Clean Energy Investor Group (CEIG), the approval timeframes for large-scale renewable energy projects in Australia are staggering1:
- Wind projects: 3,488 days (9.5 years)
- Solar projects: 705 days (2.5 years)
- Battery projects: 530 days (1.5 years)
These delays are “slowing the pace of the energy transition,” as noted by CEIG Chief Executive Simon Corbell, who advocates that project approvals should take no more than 18 months for both state and federal authorities combined2. Even less complex large-scale renewable projects require 746 days of diligent assessment and consultation. Each day represents careful consideration of environmental impacts, community needs, and long-term sustainability.
The challenge isn’t just in the timeline – it’s in the scale. Australia needs to build 10,000 kilometers of additional transmission lines3 – that’s roughly the distance from Sydney to Tokyo, except instead of crossing open ocean, these lines must thoughtfully navigate through communities, ecosystems, and existing infrastructure. Each kilometer requires careful planning, thorough environmental assessments, and meaningful stakeholder engagement. As TransGrid CEO Brett Redman emphasises, this massive infrastructure rollout requires “Olympics-style coordination” to succeed4.
1ABC News (2024). “Only three wind farms were connected to the power grid last year. Here’s what’s holding up Australia’s clean energy transition.” Retrieved from https://www.abc.net.au/news/2024-02-29/renewable-approval-delays-slowing-cleanenergy-transition/103517718
2ABC News (2024). “Only three wind farms were connected to the power grid last year. Here’s what’s holding up Australia’s clean energy transition.” Retrieved from https://www.abc.net.au/news/2024-02-29/renewable-approval-delays-slowing-cleanenergy-transition/103517718
3Energy Networks Australia (2023). “Transmission Infrastructure and Grid Connection Challenges Report.
4The Australian (2024). “TransGrid boss Brett Redman says renewables rollout requires Olympics-style coordination.” Retrieved from https://www.theaustralian.com.au/business/renewable-energy-economy/transgrid-boss-brett-redman-says-renewables-rollout-requires-olympicsstyle-coordination/news-story/61955df4d1b08207fa0cf83eec722a7a
From Patterns to Practice: The AgentForce Approach
Now, before we get carried away with visions of self-organising bureaucracies (a phrase that should strike equal parts terror and hope into the hearts of public servants everywhere), let’s ground ourselves in how Agentforce actually implements these concepts.
At its core, each “swarm” in Agentforce is built on three fundamental patterns we’ve explored in previous blogs:
- The React Pattern: Each agent observes, thinks, plans, and acts in continuous loops
- RAG-Enhanced Context: Agents maintain awareness through sophisticated retrieval systems
- Tool-Augmented Capabilities: Structured access to APIs, documents, and workflows
If you’ve ever watched a slime mold solve a maze (and let’s be honest, who hasn’t spent their Saturday nights doing exactly that?), you’ve witnessed something remarkable: a collection of simple organisms solving complex problems through parallel exploration and environmental feedback.
[Field Note: The author would like to clarify that there are, in fact, other perfectly reasonable ways to spend one’s Saturday nights. The slime molds, however, are unavailable for comment.]
This is precisely the inspiration behind the idea of a ‘process swarm’. Rather than attempting to pre-program every possible path through the bureaucratic maze, we’ve created collections of specialised agents that work in parallel, each responding to and modifying their environment as they go. But – and this is crucial – they’re not AGI or some other super-robot perfectly programmed with every conceivable path through the macro-process. They’re carefully structured implementations of the react pattern, enhanced with RAG (Retrieval Augmented Generation) for context maintenance and equipped with specific tools for their domains – but as a GROUP, something remarkable emerges.
The Anatomy of a Process Swarm
Let’s explore what this might look like in practice. A process swarm could, for instance, consist of several types of specialised agents working in concert.
We might have agents focused on navigation – understanding the current state of processes and identifying potential next steps. Others could specialise in maintaining context, using RAG to track the bigger picture across multiple processes. And some might focus on specific tool interactions, handling document processing and API calls.
Think of it as an experimental ecology, where different types of agents might evolve to fill specific niches in the process landscape. We’re still early in understanding exactly how these roles will develop and interact, but the potential is fascinating.
Specialised Swarm Architectures
While we’re still in the early stages of understanding how these patterns might evolve, we can imagine different types of swarms specialising for different domains. Think of them as potential species that could emerge to fill specific niches in our infrastructure ecosystem.
Planning Swarms: The Bureaucratic Navigators
Consider how we might approach planning regulations with a swarm-based architecture. These swarms could specialise in understanding and navigating the complex web of regulations, requirements, and dependencies that govern infrastructure development.
We might implement the React pattern in various ways here. For instance, planning agents could maintain continuous loops of observation and action, using RAG to pull relevant precedents, requirements, and contextual information from repositories of previous projects. Think of it as experimenting with different approaches to bureaucratic navigation, learning what works best through practical application.
The magic happens in how these swarms maintain context across approval stages. Using topic systems inherited from Blog 3, they create rich contextual maps of the approval landscape. When one agent discovers a successful path through a particular regulatory challenge, that information becomes part of the environmental markers that guide future navigation. Think of it as leaving breadcrumbs through the bureaucratic forest, except the breadcrumbs are sophisticated metadata about successful approval patterns.
But these aren’t just passive pathfinders. Planning swarms actively shape their environment through tool integration. Document processing tools help them understand and generate required materials. Timeline management tools let them coordinate complex sequences of approvals. And perhaps most importantly, they maintain awareness of dependencies and relationships between different aspects of the planning process.
The result? A system that doesn’t just process applications – it actively shapes the planning environment through stigmergy. Each successful approval creates markers that guide future applications, while each challenge encountered leaves environmental cues that help other agents avoid similar pitfalls. It’s rather like how termites will adjust their building patterns based on the properties of available materials, only with slightly more concern for local zoning laws.
Messaging Swarms: The Stigmergic Social Network
In the depths of the Amazon rainforest, leaf-cutter ants maintain vast networks of trails that connect their multiple nests to various foraging sites. These trails aren’t just paths – they’re dynamic information highways, constantly updated with pheromone signals that help coordinate the activities of millions of individuals.
[Field Note: Still more efficient than most corporate email chains, though with marginally more mandible-clicking.]
Our messaging swarms in Agentforce might serve a similar function, though with a particularly intriguing twist. Instead of pheromone trails, imagine a network of agents creating and responding to digital markers across the infrastructure development landscape. These wouldn’t be simple notification systems or message queues – they could form a kind of living communication fabric that adapts and evolves with each interaction.
Think about how information typically flows through infrastructure projects today: emails pile up, documents get buried in SharePoint, and crucial context gets lost between systems. But what if we could create something more organic? Drawing inspiration from how ant colonies maintain their communication networks, we might design messaging swarms that:
- Create persistent information trails through topic systems, leaving behind contextual breadcrumbs that other agents can follow
- Dynamically adjust the “strength” of different communication pathways based on their effectiveness and relevance
- Maintain awareness of both the content and the context of communications across multiple processes
- Adapt their routing patterns based on successful communication patterns
The potential here lies in how these swarms might handle complex multi-party communications. When an environmental assessment raises concerns about wildlife corridors, for instance, the messaging swarm wouldn’t just notify relevant teams – it could potentially modify the environment in ways that influence the behavior of other swarms. Planning swarms might automatically adjust their approach, while stakeholder engagement swarms prepare targeted communication materials.
But perhaps most intriguingly, these messaging swarms could learn from their own activities. Just as ant trails become more defined with repeated use, successful communication patterns might become stronger over time. A particularly effective way of routing information between planning and regulatory processes could become a preferred pathway, while less effective routes naturally fade.
This isn’t just about moving messages from A to B – it’s about creating a kind of collective organisational memory. Each successful communication pattern, each effective stakeholder interaction, each smooth handoff between processes could leave its mark on the system. Future communications might then naturally flow along these proven pathways, like water finding the most efficient route downhill.
Of course, we’re still in the early stages of understanding how to implement such systems effectively. The challenge lies not just in the technical implementation, but in ensuring these communication patterns remain governed and traceable while still maintaining their adaptive nature. It’s rather like trying to combine the efficiency of ant colony communication with the accountability requirements of a corporate audit – an interesting challenge, to say the least.
Regulatory Swarms: The Compliance Cartographers
Imagine, if you will, what might be possible if we applied swarm patterns to regulatory compliance. Picture a collection of agents, each one focused on different aspects of compliance verification – rather like a particularly thorough team of auditors, but with the ability to work continuously and in parallel.
We could potentially implement the React pattern with a focus on continuous compliance verification. Agents might maintain ongoing loops of observation and checking, using RAG to stay current with regulatory requirements across multiple jurisdictions. The goal would be to create something akin to a team of extremely thorough auditors who never sleep (though the reality might be somewhat more modest).
The tool integration for regulatory swarms could be particularly interesting to explore. We might use verification APIs to continuously check compliance across multiple dimensions, while documentation tools maintain the all-important audit trail. The implementation of topic systems could potentially create complex maps of regulatory dependencies, helping ensure that compliance in one area doesn’t inadvertently create issues in another.
When regulations change (and don’t they always?), these swarms might do more than just update a database – they could potentially modify the environment in ways that naturally guide other swarms toward compliance. A change in environmental regulations might trigger updates across multiple projects, with the regulatory swarm leaving clear markers about required adjustments.
Stakeholder Swarms: The Communication Choreographers
Perhaps one of the most intriguing possibilities comes from applying swarm patterns to stakeholder engagement. Taking inspiration from how honey bee colonies communicate through dance, we might develop systems that coordinate complex patterns of stakeholder interaction.
We could experiment with implementing the React pattern for continuous monitoring and response to stakeholder interactions, building sophisticated models of relationships and concerns. The RAG implementation might be particularly interesting here – potentially maintaining not just the history of interactions, but the emotional context and relationship dynamics that are crucial for effective engagement.
The tool integration could open up fascinating possibilities. Communication APIs might enable outreach through multiple channels, while document generation tools could help craft materials tailored to specific stakeholder needs. The use of topic systems might help maintain complex conversation trees across multiple parties, ensuring that each stakeholder’s concerns are tracked and addressed in the context of the broader project.
When a community concern emerges about a renewable energy project, for instance, the stakeholder swarm might do more than just log the issue – it could potentially modify the environment in ways that trigger adaptive responses across multiple swarms. We might see planning swarms adjusting their approach while document generation tools prepare targeted information packages.
The real magic emerges when multiple stakeholder swarms interact across different projects. They create a kind of collective intelligence about effective engagement strategies, learning from each success and failure to build an ever-more-sophisticated understanding of stakeholder dynamics. Think of it as a digital democracy in action, just with better note-taking and significantly less shouting.
The Emergence of Network Effects
Here’s where things could get really interesting. While each individual swarm would be built on these concrete patterns, something remarkable might emerge when deployed at scale across multiple projects and organisations. It’s rather like watching a coral reef develop – each individual polyp follows simple rules, but together they create something far more complex and beautiful.
When multiple organisations deploy these swarms for their infrastructure projects, we might see something extraordinary begin to emerge. Each instance could become both a producer and consumer of environmental markers – digital breadcrumbs that guide future navigation. A successful approval process in one project might leave traces that inform similar processes across the network. A particularly effective stakeholder engagement strategy could potentially become encoded into the collective memory of the system.
Think of it like the development of human cities. Each builder follows local rules and patterns, but over time, successful patterns get replicated and refined across different locations. In our vision for these systems, each swarm might:
- Create environmental markers through its actions, like ants laying down pheromone trails
- Learn from successful process patterns, much like termites adapting their building techniques
- Contribute to a growing body of process knowledge, potentially creating a kind of collective intelligence
- Adapt based on observed successes and failures across the network
This is where true emergence might begin to appear – not from any individual swarm’s behavior, but from the network effects of multiple swarms operating in parallel across different projects and organizations. Each successful navigation of a regulatory process, each effective stakeholder engagement, each optimized approval pathway could become part of a growing collective intelligence.
But – and this is crucial – this isn’t about creating some magical AI hive mind. What we’re exploring is the practical potential of implementing the React pattern at scale, enhanced by RAG for context maintenance, and coordinated through topic systems. When hundreds of planning swarms are simultaneously navigating approval processes, we might see patterns of success naturally emerge. When regulatory swarms across multiple jurisdictions encounter and solve similar challenges, their solutions could become part of the environmental markers that guide future projects.
The real power might lie in how these patterns could compound over time. Just as a termite colony becomes more efficient at maintaining its mound through successive generations, our infrastructure development processes might become more sophisticated through successive iterations. Each project wouldn’t just complete its objectives – it could contribute to a growing body of practical knowledge about what works and what doesn’t.
Implementation Framework
The key to making this work in practice lies in how Agentforce implements these patterns within enterprise constraints. Just as termites don’t need to understand architecture to build sophisticated structures, our swarms don’t need to understand the entire infrastructure development process to optimise it. What they need is a robust framework of patterns and tools that enable coordinated behavior.
The React Pattern: The Heartbeat of Swarm Intelligence
At the core of every swarm lies the React pattern we explored in Blog 2. Each agent maintains continuous loops of observation and action, like a tiny bureaucrat who actually enjoys their job. These loops consist of:
- Observation phases where agents gather current state information
- Planning phases where they determine optimal next steps
- Execution phases where they interact with tools and systems
- Verification phases where they check outcomes and adjust course
But unlike traditional automation, these loops aren’t rigid. They adapt based on environmental feedback, much like how a termite will adjust its building behavior based on local conditions. The React pattern gives our swarms the ability to maintain awareness and respond intelligently to changing circumstances.
RAG-Enhanced Context: The Collective Memory
The power of RAG (Retrieval Augmented Generation) in our swarms goes far beyond simple document lookup. As explored in Blog 1, our implementation creates a sophisticated context maintenance system that:
- Processes and indexes complex regulatory documents
- Maintains historical context across multiple processes
- Tracks the state of various approval pathways
- Maps intricate stakeholder relationships and concerns
Think of it as giving each agent access to a perfectly organised library of every relevant document, decision, and interaction – except this library updates itself in real-time and actually helps you find what you’re looking for (a marked improvement over my university days).
This is where that curious word “stigmergy” suddenly becomes more than an academic curiosity – it’s becoming a massive commercial and corporate reality. If you trace the thread through our previous blog posts, you’ll notice a recurring motif: the loop. This isn’t just any loop – it’s what transforms an LLM workflow into a true agent, what makes those simple two-dimensional diagrams suddenly pop into three-dimensional swarms of coordinated activity.
The loop is what gives the humble termite its architectural genius, because it creates that crucial input-output relationship with the environment. This environmental interaction – stigmergy – isn’t just some exotic natural phenomenon. It’s happening in enterprise systems every day. Consider your typical SaaS CRM like Salesforce: every note, every email record, every meeting transcript, every status update from A to B – these aren’t just database entries. They’re environmental markers, persistent trails left behind after the action moves on.
And here’s where RAG becomes truly transformative. It’s not just retrieving information – it’s dynamically checking the current state of the system, injecting that state back into the context of the next LLM invocation. This is why our traditional vocabulary around “learning” and “training” has started to break down. These terms now encompass something far broader than just retraining trillion-parameter models or fitting traditional machine learning algorithms.
They now include the remarkable ability of LLMs to perform in-context learning, to instantly interpret rich context in ways that govern their behavior. We humans have been getting better at marshaling institutional knowledge through tools like Salesforce and other SaaS platforms. In doing so, we’ve been unconsciously using stigmergy to our advantage, using our business environment as the ultimate memory state of our processes and practices.
This realisation isn’t just an interesting parallel – it’s why we need to rapidly embrace true agentic behaviors in 2025. This is the year when the world needs to understand how to orchestrate thousands of clever but narrow-scope agents to work together and achieve remarkable outcomes. Our termite friends have been trying to teach us this lesson all along.
Tool Integration: The Digital Opposable Thumb
Tools are what transform our swarms from clever theorists into practical problem solvers. Through carefully structured API access, they can:
- Interact with external systems and databases
- Generate and process documentation
- Manage communication across multiple channels
- Verify compliance and track progress
But here’s the clever bit – these tools aren’t just dumb utilities. They’re integrated into the swarm’s environmental awareness through the React pattern. Each tool use becomes part of the environmental markers that guide future actions.
Agent-to-Agent Handoff: The Dance of Digital Delegation
Here’s where things get particularly interesting (and where my metaphorical termites start to look suspiciously like a distributed computing lecture). Agentforce has already evolved to support agent-to-agent interactions, allowing these digital workers to collaborate in ways that would make a bee colony’s communication dance look positively primitive.
Through a combination of existing case patterns, direct API calls, and Apex integrations, we’ve created the conditions for agents to autonomously mobilise and collaborate. They can route work between themselves, maintain context across handoffs, and create entirely new patterns of interaction. It’s rather like watching a relay race where each runner gets to decide not just when to pass the baton, but who to pass it to, and what message to whisper as they do.
And here’s the mind-bending part: when you consider that each “agent” in Salesforce is itself a kind of swarm (implementing multiple patterns, maintaining multiple contexts, juggling multiple tools), you begin to see why we’re already tackling large-scale swarm patterns. We rather have to – it’s the natural evolution of complex agent systems. What looks like a single agent from the outside is often more like a specialised team working in perfect coordination, and now these teams can collaborate with other teams.
[Field Note: It’s swarms all the way down, as a particularly philosophical termite might observe…]
Future Implications
As I write this, watching a line of ants efficiently navigate around my coffee cup to reach a forgotten biscuit crumb, I’m struck by a thought: we’re only beginning to scratch the surface of what’s possible when we combine these proven patterns with large-scale deployment.
Imagine energy and utility infrastructure projects that adapt and optimise in real-time, like a living organism responding to its environment. Picture approval processes that flow as smoothly as ant trails, each success laying down markers that make the next attempt more efficient. Envision stakeholder engagement that orchestrates itself with the sophistication of a bee colony, where countless individual interactions combine into an elegant dance of communication and consensus-building.
This isn’t just about making existing processes faster – it’s about fundamentally transforming how we approach infrastructure development. Just as termites transformed the Australian landscape with their mound-building activities, these digital swarms have the potential to transform our infrastructure development landscape.
The future of infrastructure development might look less like a traditional construction project and more like a living system – adaptive, resilient, and incredibly efficient. And if that sounds a bit too poetic for your taste, well, I suggest spending an evening watching a termite colony at work. Nature, it turns out, might have been trying to teach us something about infrastructure development all along.
Welcome to the age of stigmergic energy and utility infrastructure development. The termites would be proud.
