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·14 min read·agentic-ai·agi·ai-agents·software-engineering·governance
Cristiano Ferreira

The Agentic AI Era: are we heading toward AGI or a new architecture of work?

If you've followed the evolution of artificial intelligence over the past two years, you've probably noticed that the conversation has shifted.

In the beginning, we talked a lot about generative models capable of answering questions, writing text, summarizing documents, generating images, or supporting programming tasks. It was a powerful AI, but still essentially reactive: the human asked, the AI answered.

Now, the conversation is changing.

The most relevant trend for 2026 may not simply be "bigger models" or "better answers." The most important movement seems to be the transition to agentic AI systems: agents capable of interpreting objectives, breaking down tasks, using tools, accessing files, calling APIs, executing commands, validating results, and continuing workflows with some degree of autonomy.

This raises an important provocation:

Are we really heading toward a single, universal, centralized AGI? Or are we building a mesh of specialized agents, connected to real tools, operating processes under human governance?

This question matters because it completely changes how companies, technical leaders, and technology professionals need to look at AI.

1. From AI that answers to AI that operates

The big change is not just AI writing better text or generating higher-quality code. The structural change is that AI is starting to move from the role of assistant to the role of operator.

OpenAI introduced workspace agents in ChatGPT, shared agents within organizations capable of handling complex workflows and long-running tasks, operating within company-defined permissions and controls. The product description itself makes this movement clear: agents that can write or execute code, use connected applications, remember learnings, and continue multi-stage work.

This changes the question we ask AI.

Before, the interaction was:

  • "Write an email."
  • "Summarize this document."
  • "Create a code snippet."
  • "Explain this error."

Now, the direction is shifting:

  • "Prepare the analysis."
  • "Review this process."
  • "Run this validation."
  • "Compare the scenarios."
  • "Open the necessary tickets."
  • "Monitor this flow."
  • "Propose a fix and validate with tests."

The difference is subtle but profound.

AI stops being just a conversational interface and starts becoming an operational layer over systems, data, tools, and processes.

2. The "agentic enterprise" and the shift in operating models

Companies like Salesforce and Google Cloud have been increasingly using the idea of the agentic enterprise. Salesforce, for example, published trends for 2026 highlighting themes like ambient intelligence, semantic layers for agent collaboration, simulation environments, and the evolution of enterprise agents.

In a more recent article, Salesforce describes trends such as deterministic guardrails, context engineering, "headless" CRM access, and new organizational roles tied to agent operations.

Google Cloud also published a report on AI agent trends for 2026, positioning agents as central elements for business transformation, workflows, and value generation.

In practice, this indicates that the market is not treating agents as just "better chatbots." It's treating agents as a new layer of corporate automation.

And here's an important point: you can't just layer AI on top of a broken process and expect excellence.

Agents need context, reliable data, well-defined permissions, system integration, observability, audit trails, and control mechanisms. Without these, the agent just accelerates disorganization.

3. Multi-agents: a new way of thinking about digital teams

Another relevant point is the evolution from individual agents to multi-agent systems.

Microsoft published an interesting approach on three levels of agentic AI and when not to use agents. The article reinforces that not every problem needs an agent. Sometimes a traditional workflow, a deterministic automation, or a well-implemented business rule works better. But when there's a need for reasoning, tool use, dynamic context, and multi-step decision-making, agents start to make more sense.

This point is essential.

The trend is not to create a "magic agent" for everything. The more mature trend seems to be creating specialized agents:

  • a requirements agent
  • an architecture agent
  • a code agent
  • a testing agent
  • a security agent
  • a documentation agent
  • a support agent
  • a data analysis agent
  • an orchestrator agent

This is much closer to a distributed digital organization than a traditional chatbot.

In this scenario, the human role changes. The professional stops being just an executor and starts acting more as an architect, supervisor, validator, and orchestrator.

It's not a simple replacement. It's a change in function.

4. Coding agents as a laboratory for autonomy

Software development has become one of the main laboratories for agentic AI.

This happens because code has an important advantage: it's possible to validate results with tests, builds, linting, static analysis, execution, review, and objective behavior comparison.

Anthropic published an analysis on agent autonomy in real-world usage, observing interactions with Claude Code and the API. One of the signals cited is that between October 2025 and January 2026, the duration of the longest turns increased significantly, indicating that users are delegating more extensive tasks to agents.

Anthropic also published a report on agentic coding trends for 2026, pointing to agents capable of working for hours or days, collaborating with humans, knowing when to ask for help, and handling more complex engineering tasks.

This point seems decisive: an agent's maturity is not just about "getting an answer right," but about being able to execute a useful, verifiable, and secure work sequence.

The question shifts from:

  • "Does the model seem intelligent?"

To:

  • "Can it work with enough autonomy, validate what it did, ask for help when needed, and leave auditable traces?"

5. AGI: timeline or practical impact?

There's a big debate around the date of AGI.

Some leaders and investors make aggressive predictions. Others are more conservative and point to longer horizons. Forbes published predictions for 2026 highlighting the expansion of agentic AI in logistics, production, workflows, and hybrid teams.

But perhaps the most important question is not "what year does AGI arrive?"

The question might be:

If specialized agents can already automate a significant portion of intellectual work, doesn't the practical impact start before full AGI?

Even if we don't have an AGI in the classical sense, a general intelligence capable of performing any human intellectual task, we can already have systems strong enough to transform entire areas.

Customer service. Software engineering. Data analysis. Financial operations. Legal. Marketing. HR. Supply chain. Security. Documentation. Technical support.

Perhaps the impact comes before the formal definition.

In other words: we may not have "full AGI," but we can have functional AGI by domain.

6. Security: when the error stops being just a bad answer

The more autonomy we give agents, the greater the operational risk.

A traditional chatbot can get an answer wrong. An agent can get an action wrong.

It can call an unauthorized API, modify a record, send a message, execute a command, consume budget, access sensitive data, or make a decision outside the expected scope.

That's why security in agentic AI cannot be treated as an afterthought.

Microsoft published guidance on end-to-end agentic AI security, highlighting the need to protect agents, their foundations, and the environments where they operate.

Microsoft also addressed OWASP risks for agentic AI, including issues like tool abuse, context manipulation, unauthorized access, unauthorized actions, and risks associated with operating agents connected to real systems.

This is where a discipline that will likely be central in the coming years enters: agent governance.

This includes:

  • minimal permissions
  • per-agent identity
  • scope of action
  • sandbox environments
  • logs
  • human approval for critical actions
  • cost limits
  • tool control
  • continuous evaluation
  • post-production monitoring
  • interruption capability

Agent architecture needs to be born with security, not receive it after the fact.

7. The parallel with agility: agents change the delivery cycle

There's also an interesting parallel with agile methods.

For years, we organized technology around squads, backlogs, sprints, dailies, planning, review, retrospective, and delivery metrics. This model was built to coordinate human work in complex environments.

But what happens when a significant portion of execution starts being done by agents?

  • The backlog changes.
  • Prioritization changes.
  • Cycle time changes.
  • The definition of done changes.
  • The daily changes.
  • The review changes.
  • Governance changes.

An agent can break a story into tasks, propose implementation, run tests, document decisions, and suggest improvements. But this doesn't eliminate the need for method. On the contrary: it increases the need for clarity.

The more agents enter the process, the more important it becomes to define:

  • what is the objective
  • what is the scope
  • what are the acceptance criteria
  • what is the autonomy limit
  • which actions require human approval
  • what evidence proves the task was completed
  • who is accountable for the final result

In this sense, agentic AI doesn't "kill" agile. It forces an evolution.

Agile shifts from being just coordination of people to also being coordination of people, agents, tools, and automated validations.

8. The human role: from executor to maestro?

The final provocation is about our role.

If agents start executing parts of the work, the human differentiator stops being about repetitive execution. It shifts to problem definition, judgment, ethics, context, strategy, prioritization, and responsibility.

It's not just about asking better questions to AI.

It's about knowing how to design systems where AI can operate safely and generate real value.

Technology can play some instruments on its own. But we still need to decide which music should be played, in what context, with what limits, and with what responsibility.

That's why studying AGI in 2026 is not just about studying language models.

It's about studying architecture. It's about studying governance. It's about studying security. It's about studying work. It's about studying strategy. It's about studying economic and social impact. It's about studying how humans and agents start collaborating in increasingly complex systems.

Perhaps AGI, when viewed from the market, won't first appear as a "universal brain."

Perhaps it will appear as a network of specialized agents, integrated with real tools, operating entire workflows under human supervision.

And perhaps this is the most important change of this moment:

We are leaving the era of AI that only answers and entering the era of AI that starts to operate.

The question is: are our companies, architectures, and leadership models ready for this?

See how DevAgents OS operates with AI agents across the engineering cycle →

References

_Published May 18, 2026_