Agentic AI

From research and software development to decision-making and business operations, multi-agent systems (MAS) offer the potential for scalable, persistent, and self-organizing collaboration. Yet, as we stand at the frontier of this transformation, we must reckon with a fundamental truth: many of the challenges these agents will face are not new—they are reflections of long-standing shortcomings in human collaboration and software engineering.

If we struggled to align humans to a shared goal, coordinate across silos, or articulate clear intent in requirements, why would it be fundamentally different for a group of autonomous agents? Without intentional design, governance, and alignment, agentic systems will not circumvent these issues, they will amplify them.

At Trisotech, we believe it won’t be different unless it is designed differently. Our Digital Enterprise Suite, Semantic Modeling powered by AI-Augmented Reasoning and Technology (SMART), and Decision-Centric Orchestration capabilities are engineered precisely to address these issues, by making intent explicit, processes transparent, and agentic collaboration governable.

We discuss below why agentic AI must be guided by the lessons of the past if it is to accelerate, rather than entangle, human endeavors.

1. Process Management and the “White Space” Problem

Human Context:

Organizations introduced business process management (BPM) to address the “white space” between organizational silos, those undefined responsibilities where collaboration breaks down. Processes ensured that work flowed across roles in pursuit of collective goals.

Agentic Implication:

In multi-agent systems, similar white spaces emerge, gaps in responsibility, unclear handoffs, conflicting priorities between autonomous agents. Like humans, agents require coordination models, role definitions, and governance frameworks to ensure coherent behavior across systems. Otherwise, their autonomy becomes fragmentation.

2. Requirements Engineering and Intent Specification

Human Context:

One of the most chronic failures in software engineering is poor requirements specification. Human stakeholders often cannot fully articulate their goals or constraints, leading to mismatched systems and rework.

Agentic Implication:

Agents rely on goal specifications, prompts, or policies to operate. But these, too, suffer from underspecification, ambiguity, and misalignment. If humans struggle to communicate intent to each other, they are unlikely to fare better communicating it to agents, especially in dynamic or ill-structured domains.

3. Organizational Politics and Misaligned Incentives

Human Context:

Teams often underperform not due to lack of talent but due to misaligned incentives, siloed metrics, or political friction. Collaborative success hinges on aligning individuals to shared purpose and outcomes.

Agentic Implication:

Multi-agent systems require mechanisms for conflict resolution, prioritization, and utility alignment. Without shared reward functions or arbitration protocols, agents may act at cross-purposes, compete for resources, or exploit loopholes in their reward structures, just as humans do.

4. Over-Automation and Brittleness in Complex Systems

Human Context:

Over-automated business systems have historically failed to adapt to edge cases or exceptions, especially when context is lacking. Human judgment is often needed in complex, ambiguous situations.

Agentic Implication:

Agentic systems risk similar brittleness. When agents are overconfident, underinformed, or misaligned, their decisions can cascade into systemic failure. We must ensure mechanisms for escalation, human override, and continuous learning.

5. Knowledge Fragmentation and Semantic Drift

Human Context:

In large-scale organizations, knowledge often becomes fragmented, residing in documents, systems, or individuals, leading to inconsistency and misunderstanding over time (semantic drift).

Agentic Implication:

Agents must share a common semantic understanding, ontologies, vocabularies, context models. Without shared knowledge structures, agents will interpret the same inputs differently, fail to interoperate, and compound semantic errors at scale.

6. Coordination Overhead and Scaling Challenges

Human Context:

Adding more people to a project does not linearly increase productivity (Brooks’ Law). Coordination, communication, and integration costs grow nonlinearly.

Agentic Implication:

Adding more agents may increase functional capacity but can exponentially increase orchestration complexity. Decentralized control requires robust protocols for delegation, synchronization, consensus, and trust.

Recommendations for Human-Agent Collaboration at Scale

To leverage agentic AI effectively, we must engineer collaboration, not just autonomy. This includes:

• Structured Orchestration: Just as BPM brought structure to human collaboration, agentic systems require workflows, protocols, and decision models (e.g., BPMN, CMMN, DMN) to coordinate agents.
• Explicit Intent Modeling: Use formal intent-capture models (e.g., goal hierarchies, decision models, process models and case models) to bridge human-agent understanding.
• Governance and Oversight: Establish boundaries, escalation rules, audit trails, and override mechanisms to ensure accountability.
• Shared Semantics: Develop and maintain domain ontologies, knowledge graphs, and context models for semantic coherence.
• Agent Design Patterns: Use design patterns for inter-agent communication (contract nets, publish/subscribe, mediator) and distributed decision-making.

Conclusion

The rise of agentic AI offers a profound opportunity to accelerate human endeavors, but not by escaping the challenges of human collaboration. Rather, it forces us to confront them in a new, programmable form. But it also offers an opportunity to approach these problems with greater precision, transparency, and automation.

If we learn from history and design with foresight, agentic systems can become amplifiers of collective intelligence. If not, they may merely reproduce our blind spots in silicon.