Mind Over Modules: How Smart Agents Learn What to See—and What to Be

In the race to build more autonomous, more intelligent AI agents, we’re entering an era where “strategy” isn’t just about picking the next move—it’s about choosing the right mind for the job and deciding which version of the world to trust. Two recent arXiv papers—one on state representation in dynamic routing games, the other on self-generating agentic systems with swarm intelligence—show just how deeply this matters in practice.

We’re no longer only asking: What should the agent do? We now must ask:

• How should the agent perceive its environment?
• How should the agent assemble and adapt its own internal modules?
• Where do failures and breakthroughs actually originate—in what is seen, or in who is seeing?

When the Map Misleads the Navigator: State Representation in Action

Consider a seemingly simple multi-agent traffic routing game: several AI drivers must choose routes through a city network. The rules are fair, and everyone can “see” the same information—or so it seems.

The paper “The Effect of State Representation on LLM Agent Behavior in Dynamic Routing Games”¹ demonstrates that how you present the road network—whether as a set of counts, full edge lists, or partial neighborhood data—completely changes what strategies emerge.

Example 1: Scalar Count vs. Edge List

• If agents receive a scalar count of vehicles per route, they tend to herd together, creating jams, since everyone reacts to the same simple KPI.
• If given a graph of edges and explicit connections, agents might discover cooperative equilibria, splitting flows or finding underused routes—sometimes even “inventing” traffic-light-like conventions.

Example 2: Information Hiding and Incentive Distortion

• When information is partial (e.g., only local congestion stats), agents can become short-sighted—optimizing for personal gain but creating systemic gridlock.
• Business analogy: A dashboard that only shows “total revenue” but not profit per product might make an agent over-invest in loss leaders.

Takeaway: The map shapes the journey. When building AI copilots or workflow bots, what information abstraction you choose isn’t neutral—it’s as decisive as any algorithm tweak.

When the Navigator Builds Itself: SwarmAgentic and Modular Evolution

Now flip the problem: what if, instead of changing the map, you let the agent’s internal architecture evolve? This is the promise of “SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence”².

SwarmAgentic in a Nutshell

• Agents are constructed from a “parts library” of modules—memory, planning, perception, tools—assembled automatically.
• A swarm intelligence algorithm generates many candidate agents, which then compete and adapt through evolutionary loops, guided by an LLM “orchestrator.”
• Agents that perform well on a suite of diverse tasks survive and “reproduce” their design elements, like digital natural selection.

Example 1: Modular Tool Use

• Some agents evolve to prioritize tool-calling abilities, becoming plugin-savvy super-connectors (imagine an AI personal assistant that discovers and wires up new SaaS APIs on its own).
• Others become minimalists, relying on internal reasoning and memory—better for lightweight edge deployments.

Comparison: Hand-Crafted vs. Emergent Architecture

• Hand-crafted agent: Rigid, maybe well-tuned for known problems, but struggles with “unknown unknowns.”
• Evolved agent: May look messy under the hood, but can “discover” modular tricks or hybrid strategies that human designers never anticipated.

Business analogy: Instead of hiring specialists for each workflow, imagine letting a team self-organize roles until the most effective org chart emerges—then let them keep reorganizing as new challenges arrive.

Bridging the Two: The Reflexive Agent in Practice

Bringing these insights together, the next frontier is a reflexive agent—one that can co-evolve its perception pipeline and its architecture.

Example: Diagnosing Failure—Is It Me, or the Map?

• Suppose a supply chain AI keeps making poor warehouse allocations.

  • Is the problem because it lacks modules for long-term forecasting? (architectural issue)
  • Or is it seeing only “average delivery time” and missing SKU-level bottlenecks? (representation issue)

• A reflexive agent could run internal A/B tests, switching both modules and input formats, then statistically link failures to either flawed self-design or misleading inputs.

Comparison: Rigid vs. Reflexive Systems

• Rigid system: Blames “data quality” or “user configuration” for errors, never questions its own internal machinery.
• Reflexive system: Runs experiments on itself, asks “what if I saw things differently—or had a new tool inside?”

Implications for Business Automation and AI Design

This isn’t just academic. In real-world BPA and enterprise AI, most failures are not technical but structural:

• Example: A CRM bot optimizing for “emails sent” rather than “meetings booked” because its dashboard hides conversion rates—leading to busywork, not business.
• Example: Automated trading systems locked into single-indicator strategies, unable to combine new market signals or rewire their own logic as volatility regimes change.

How to future-proof?

1. Flexible Perception: Let agents suggest new input features or dashboards when their performance degrades.
2. Modular Evolution: Allow agent architectures to adapt by borrowing modules from high-performing peers.
3. Reflexive Diagnostics: Give agents incentives to diagnose whether they need a new “lens” or a new “brain.”

Final Thought: From Strategy to Self-Discovery

The most advanced AI agents of tomorrow won’t just follow orders or optimize KPIs. They’ll explore who they could be—testing new ways to see, to think, and to act.

Strategy will no longer mean “the best move given the rules,” but “the best way to rewire myself and my world model to master the rules that matter.”

That’s the Cognaptus vision for agentic AI: not just to automate tasks, but to automate self-improvement and self-understanding.

Cognaptus: Automate the Present, Incubate the Future.