Beyond API-First: The Agentic Swarm

API-first architecture was supposed to be revolutionary. Design the interface before the implementation. Break monoliths into microservices. Make everything composable. And it was revolutionary—for about five minutes.

Then we realized microservices were just distributed monoliths. Then we discovered Mixture of Experts (MoE) models that route requests to specialized sub-models. Then we built systems that orchestrate multiple AI instances across distributed hardware.

And suddenly, API-first architecture looks quaint. Like being proud you upgraded from a mainframe to a client-server model while the rest of the world moved to edge computing.

The future isn't API-first. It's agent-first. And it's already here.

The API-First Illusion

Here's what an API-first architecture (even if it's only a concept or sketch) gets right: designing interfaces before implementation forces you to think about contracts, composability, and separation of concerns. This is good engineering.

Here's what it misses: APIs assume you know what you want to call and when.

That assumption breaks down when your "services" are intelligent agents that:

• Decide for themselves when they're needed
• Negotiate with other agents about task allocation
• Spawn new specialized agents on demand
• Route work based on current system load, not predetermined logic
• Learn from previous interactions to optimize future ones

You can't design an API for that. The interface emerges from the agents' interactions.

I'm Not a Developer. I Built APIs Anyway.

Full disclosure: I'm not a software developer. Never have been. But I built OCR and Redaction APIs for 4Square Innovations' AI application AskDiana (part of 4Square Capital).

Why? Because APIs are the minimum viable abstraction for making specialized capabilities accessible. You don't need to be a developer to understand that an OCR service should take an image and return text. That's the beauty of API-first thinking.

But here's what I learned building those APIs: the hard part isn't the interface, it's the intelligence.

The OCR API doesn't just extract text—it makes decisions about:

• Which OCR engine to use for this specific document
• Whether to pre-process the image and how
• How to handle tables versus paragraphs versus forms
• When confidence is too low and human review is needed

The Redaction API doesn't just black out text—it understands context:

• Is this Social Security number actual PII or part of a policy discussion?
• Should addresses be redacted differently than phone numbers?
• Are there jurisdiction-specific regulations that apply?
• How aggressive should redaction be for this document type?

These aren't passive services waiting for API calls. They're intelligent agents making nuanced decisions. The API is just the doorway.

The Evolutionary Path We're Actually On

Let's trace the real evolution:

Monolithic Applications: Everything in one codebase. Tight coupling. Shared state. Change anything, test everything, deploy everything.

API-First / Microservices: Break the monolith into services. Define interfaces. Scale independently. Deploy independently. Good! But still fundamentally orchestrated from above.

Mixture of Experts (MoE): Smart routing to specialized sub-models. The system learns which expert to use for which input. Getting warmer—but still a single coordinating intelligence making routing decisions.

Multi-Agent Orchestration: Multiple AI systems coordinated by a conductor. I wrote about this in AI Everywhere—using one Claude instance to coordinate Claude, Gemini, and Ollama, each contributing their strengths. Better! But still centrally orchestrated.

Distributed Agentic Swarms: And here's where it gets interesting.

What Comes After Orchestration

In my AI Everywhere experiments, I built systems where multiple AI instances work together. One approach used tmux to run Claude, Gemini, and Ollama side-by-side, all sharing context. Another distributed Claude instances across Raspberry Pis using a file-based queue system.

Both were orchestrated. There was a "boss" making assignments.

But imagine if there wasn't. Imagine if the agents:

• Claimed work based on their capabilities - "I'm the OCR specialist, I'll take that document"
• Negotiated who handles edge cases - "This is 60% table, 40% text—OCR agent and table extraction agent coordinate"
• Spawned temporary specialists on demand - "This is a German medical form, spawning German-medical-OCR agent"
• Learned from each other's successes - "OCR agent handled this type well, I'll route similar documents there"
• Self-healed when agents failed - "OCR agent 1 is down, OCR agent 2 claiming its pending work"

No central orchestrator. No predetermined routing logic. Just agents, capabilities, and emergent coordination.

This is what comes after API-first.

The Architecture That Emerges

What does an agentic swarm architecture actually look like?

1. Capability Advertisement

Instead of service registries with endpoint URLs, agents advertise capabilities:

{
  "agent_id": "ocr-specialist-07",
  "capabilities": ["ocr", "document-layout", "table-detection"],
  "languages": ["en", "es", "fr", "de"],
  "document_types": ["pdf", "image", "scan"],
  "current_load": 0.3,
  "confidence_threshold": 0.85
}

Other agents discover what's available and route work accordingly. No hardcoded API endpoints. No load balancers. Just agents finding the best match for the task.

2. Intent-Based Requests

Instead of calling specific APIs, you broadcast intents:

{
  "intent": "extract_structured_data",
  "input": "german_medical_form.pdf",
  "requirements": {
    "accuracy": "high",
    "pii_handling": "gdpr_compliant",
    "response_time": "< 30s"
  }
}

Agents that can fulfill the intent claim it. Maybe OCR-specialist-07 teams up with German-language-specialist-03 and GDPR-compliance-agent-12. You don't care. You stated the intent, the swarm figured out the team.

3. Emergent Specialization

Over time, the swarm learns. Some agents get really good at specific edge cases. They start claiming those tasks preferentially. The system develops specialists without anyone programming them.

This is biological evolution in silicon. Successful patterns replicate. Unsuccessful ones fade. The architecture evolves.

4. Redundancy Through Multiplication

In API-first architectures, redundancy is expensive. You duplicate services, configure load balancers, manage failover.

In agentic swarms, redundancy is inherent. Ten agents with OCR capability? Great. If three fail, seven remain. New agents can spin up and advertise capabilities. The swarm adapts.

5. Context Inheritance

When an agent spawns a specialist or delegates to another agent, context flows naturally. Not through API parameters, but through shared understanding.

"Here's what I know about this document. I need help with the table extraction. Taking over from here."

The handoff is semantic, not syntactic.

Why This Matters Beyond AI

You might think: "Okay, cool for AI agents. But my business logic isn't intelligent."

Here's the thing: it will be.

Every "service" in your architecture will eventually have an AI component:

• Your payment processor will fraud-check using ML models
• Your inventory system will predict demand and auto-reorder
• Your customer service API will understand intent and route intelligently
• Your security layer will detect anomalies in real-time

Once your services are intelligent, orchestrating them with predetermined API calls is limiting. They should negotiate, coordinate, and optimize themselves.

The Practical Path Forward

I'm not suggesting you throw out your APIs tomorrow. Evolution, not revolution.

But start thinking about:

Making your services smarter. Add intelligence at the boundaries. Let services make decisions about how to fulfill requests, not just execute predetermined logic.

Loosening orchestration. Where you have hard-coded routing logic, introduce heuristics. Where you have heuristics, introduce learning. Let the system discover optimal paths.

Building for emergence. Design systems that can discover new patterns. Don't pre-define all interactions—create the conditions for useful interactions to emerge.

Embracing redundancy. Multiple agents with overlapping capabilities aren't waste—they're resilience. They're evolution.

Advertising capabilities, not endpoints. Shift from "here's my API schema" to "here's what I'm good at." Let callers discover the best match.

AskDiana: A Case Study

The AskDiana system I work with at 4Square Innovations is moving in this direction.

Originally: monolithic document processing. Then: microservices with OCR API, Redaction API, NLP API. Now: intelligent agents that coordinate.

When a complex financial document arrives:

1. Document classifier agent determines type and route
2. OCR agent assesses which engines to use and orchestrates them
3. Redaction agent identifies PII and determines jurisdiction-specific rules
4. Extraction agent pulls structured data and validates against known patterns
5. QA agent reviews confidence scores and flags for human review if needed

These agents don't follow a predetermined workflow. They negotiate based on the document characteristics. The architecture adapts to each document.

That's the future. Not predetermined API calls, but intelligent negotiation.

The Philosophical Shift

API-first architecture came from a control mindset: "I will define exactly how you interact with my service."

Agent-first architecture comes from an emergent mindset: "I will advertise what I'm capable of, and we'll discover together how best to work."

This shift mirrors broader changes:

• From command-and-control to coordination and emergence
• From predetermined workflows to adaptive processes
• From rigid interfaces to negotiated interactions
• From centralized orchestration to distributed intelligence

It's not just a technical shift. It's a philosophical one.

Beyond Even Swarms: What's Next?

If I'm being honest—and I usually am—even agentic swarms aren't the endpoint. They're another stepping stone.

The real future? Systems we can't fully predict or control. Systems that evolve faster than we can understand. Systems where the line between "infrastructure" and "intelligence" dissolves completely.

We're building ecosystems, not applications. Ecosystems where:

• Agents spawn, evolve, and terminate based on environmental pressures
• Successful patterns replicate across the ecosystem
• Competition for resources drives optimization
• Symbiotic relationships emerge between specialized agents
• The whole system exhibits behaviors nobody explicitly programmed

Sound scary? Maybe. But it's also inevitable.

We went from monoliths to microservices to MoE to multi-agent orchestration to agentic swarms to... evolutionary computational ecosystems.

Each step: more distributed, more intelligent, more emergent, more unpredictable, more powerful.

The Uncomfortable Truth

Here's what keeps me up at night: We're going to build these systems whether we understand them or not.

The competitive pressure is too great. The efficiency gains too compelling. The capability advantages too significant.

Companies that stay with API-first architectures will lose to those running agentic swarms. Those running simple swarms will lose to those with evolutionary ecosystems.

We're in an arms race. Not against each other, really. Against the limits of what we can manually design and understand.

The winning strategy isn't to build systems we fully comprehend. It's to build systems that can evolve beyond our comprehension while remaining aligned with our goals.

And if that sounds like AI alignment research... well, yes. Architecture and AI ethics are converging.

What You Should Do Tomorrow

Practically speaking, if you're building systems today:

Stop thinking about services. Start thinking about agents. Even if they're not AI-powered yet, design them as autonomous entities with capabilities and goals.

Build for redundancy and competition. Multiple implementations of similar capabilities aren't waste—they're evolutionary pressure toward excellence.

Loosen coupling everywhere possible. Hard dependencies are death in evolutionary systems. Everything should negotiate, not demand.

Instrument everything. You're going to need observability when behaviors emerge that nobody programmed. You need to understand what happened and why.

Embrace emergence, but set boundaries. Let the system evolve, but within constraints. Like evolution in nature—immense creativity within the laws of physics.

The Meta-Lesson

This entire article was researched and written through a process that embodies what I'm describing.

I used Claude to coordinate research. Gathered context from multiple sources. Synthesized perspectives. Let themes emerge from the material rather than imposing a predetermined structure.

The architecture of how this article was created mirrors the architecture it describes. That's not accidental.

When the process of creation matches the subject of creation, you're probably onto something real.

Conclusion: The Evolution Accelerates

API-first architecture was good. Microservices were better. MoE models showed us glimpses of intelligent routing. Multi-agent orchestration proved coordination was possible.

Agentic swarms are next. Evolutionary computational ecosystems after that. And then... honestly, I'm not sure. Systems that blur the line between built and grown? Between designed and discovered? Between controlled and guided?

What I do know: We're not going back to monoliths. We're not stopping at microservices. We're not even stopping at agent orchestration.

We're headed toward systems that are:

• Distributed across infrastructure we don't fully control
• Redundant through multiplication, not duplication
• Expert through evolution, not programming
• Agentic in decision-making, not just execution
• Flexible enough to evolve faster than we can redesign them

The APIs won't go away. But they'll become implementation details. The real architecture will be above them—in the intelligence layer where agents negotiate, coordinate, compete, and evolve.

And if you think I'm exaggerating how fast this is happening, remember: I'm not a developer, and I built intelligent APIs for production systems. The barrier to entry keeps dropping. The capability keeps rising. The pace keeps accelerating.

API-first was the beginning. Agent-first is the present. What comes after is being built right now, probably by someone who'll read this and think "that's obvious."

To them I say: yes. It is obvious. The question isn't whether this is coming. It's whether we're ready.

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