Opening — Why this matters now

Agentic AI is having its moment. Everyone wants a tireless digital employee: planning trips, fixing calendars, routing emails, “just getting things done.” But as we rush to automate autonomy, we’re discovering that many of these agents are less like seasoned professionals and more like interns with infinite confidence and unreliable memory. They improvise. They hallucinate. They negotiate with the wrong people. And, spectacularly, they don’t understand the social world they operate in.

The paper Agentifying Agentic AI fileciteturn0file0 argues a simple but uncomfortable point: today’s fashionable “agentic” systems have forgotten 30 years of research on actual agency. If we want real autonomy — the kind that doesn’t swing between brittle and chaotic — we must restore the discipline, structure, and social intelligence that the AAMAS community spent decades refining.

Background — Context and prior art

Before LLMs, agency meant something specific. Belief–Desire–Intention (BDI) architectures defined why agents act. Communication protocols like FIPA-ACL ensured messages meant the same thing to sender and receiver. Norms, institutions, and mechanism design embedded agents in social environments with expectations and rules.

These ideas were not theoretical ornamentation. They existed because autonomy in open systems is hard. Without structured reasoning, agents misinterpret context, violate norms, and fail to coordinate. The irony is that LLM-driven agents re‑introduce these classic failures — but at scale, and with a veneer of linguistic competence.

Analysis — What the paper does

The authors contrast two paradigms:

  • AAMAS agents — explicit, structured, transparent.
  • LLM-driven agentic AI — implicit, flexible, but unpredictable.

Their argument: to transform today’s “agentic” systems into actual agents, we need to hybridize. That means keeping the generative power of LLMs, but grounding them in the architectural clarity, social semantics, and coordination machinery that classical agents used.

The paper examines seven critical gaps:

1. Reliability & grounding

LLMs imitate intelligence rather than reason about the world. They can schedule a meeting with the wrong person because that name appeared in context. They can confidently produce false facts. Without grounding, autonomy becomes stochastic theatre.

2. Long-horizon agency

LLM agents handle short plans but unravel under sustained objectives. No durable commitments. No principled memory. No strategy beyond the next token.

3. Evaluation

There is no consensus on how to measure autonomy, safety, or accountability. Benchmarks are fragmented and short-horizon.

4. Governance & risk

Who is responsible when an agent acts continuously and independently? We lack the analogue of institutional oversight for digital actors.

5. Security & privacy

Agents with tool access widen the attack surface. Inputs can be manipulated; actions can be exploited.

6. Value & maturity

The majority of agentic projects fail to deliver stable value. Maintenance costs balloon. Expectations run ahead of capabilities.

7. Cost structures

Every LLM call has a price. An agent that “thinks” too much becomes economically unviable.

Against these, the paper positions AAMAS concepts — BDI, communication semantics, negotiation frameworks, norms, trust, game theory — as structural scaffolding. Not replacements for LLMs, but constraints that enable reliability.

Findings — A comparative framework

Below is a distilled version of the paper’s conceptual mapping.

Table — What AAMAS provides vs. what Agentic AI lacks

AAMAS Concept What It Offers Status in Agentic AI Why It Matters
BDI Architecture Transparent reasoning; explicit goals Absent Enables verifiability and coherent action
Communication Protocols Semantics, commitments Natural language only Reduces miscommunication and ambiguity
Mechanism Design Incentive alignment Rarely used Prevents multi-agent chaos
Coordination Frameworks Joint plans, distributed tasks Shallow/emergent Supports reliable teamwork
Negotiation & Argumentation Structured conflict resolution Missing Required for real collaboration
Norms & Institutions Social grounding Missing Critical for safety and compliance
Trust & Reputation Memory of reliability Missing Required for long-term autonomy
Social Choice & Game Theory Collective decision-making Not used Keeps multi-agent dynamics stable

The authors highlight a recurring pattern: agentic AI rediscovered autonomy, but forgot society. Real agency is relational. Without social structure, it devolves into isolated optimization.

Implications — Why businesses should care

For enterprises exploring automation, the message is clear: agentic AI is not a magic worker. It is an architecture problem.

1. Without structure, autonomy becomes liability

Agents that cannot explain themselves, coordinate, or follow norms are a governance nightmare.

2. Hybrid architectures will dominate

The future is not “LLM as the agent,” but LLM inside the agent, embedded within explicit plans, goal structures, and constraints.

3. Multi-agent design is unavoidable

In businesses, agents coordinate with:

  • human employees,
  • other agents,
  • legacy systems,
  • institutional policies.

Ignoring this reality guarantees failure modes.

4. Compliance must be designed-in, not patched-on

Norms, institutional rules, and auditability are not extras. They’re prerequisites for using autonomous systems in regulated contexts — finance, supply chain, healthcare, public services.

5. ROI depends on restraint, not improvisation

The most valuable agents will be the “boring” ones: predictable, verifiable, and well-scoped.

Conclusion

Agentic AI is exciting, but excitement doesn’t make systems safe, stable, or socially competent. The paper’s core message is refreshingly grounded: if we want autonomous AI to act responsibly in human environments, we must reintroduce the social, structural, and governance foundations that earlier generations of AI painfully learned.

In other words: the future of agentic AI will look less like a swarm of improvisational digital interns, and more like a well‑run institution — rules, roles, commitments, and all.

Cognaptus: Automate the Present, Incubate the Future.