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What Is Agentic AI — And Why It Changes Everything

Jordan Lee·November 15, 2025·4 min read

Most people think of AI as a very fast answering machine. You give it a question, it gives you an answer. That mental model is wrong — and it's holding teams back from unlocking what modern AI is actually capable of.

The Shift from Reactive to Proactive

Traditional AI systems are reactive. They wait for input, process it, return output, and stop. They have no memory of what happened five minutes ago. They don't know what they're supposed to be working on. They certainly can't go get information on their own.

Agentic AI is different. An agent:

  1. Has a goal — not just a prompt
  2. Plans how to achieve that goal across multiple steps
  3. Uses tools — web search, code execution, database queries, API calls
  4. Observes the results of its actions and adapts
  5. Self-corrects when something goes wrong

This sounds deceptively simple. The implications are enormous.

A Concrete Example

Say you need to produce a competitive analysis of five companies every Monday morning. With a traditional LLM setup, you'd write a prompt, paste in some data, and get a response. You'd still need to manually gather the data, run the prompt, format the output, and deliver it somewhere useful.

With an agentic system, you describe the goal once. The system:

  • Wakes up at 6am Monday
  • Queries financial databases for the latest metrics
  • Scrapes public earnings calls and press releases
  • Runs sentiment analysis on recent news
  • Cross-references against your internal CRM
  • Writes the report in your company's format
  • Posts it to Slack and emails the relevant stakeholders

No human involved after the initial setup. And if the SEC filing API is down, it routes around it and notes the gap in the report.

Multi-Agent Systems: Where Things Get Interesting

Single agents are useful. Multi-agent systems are transformative.

When you have agents that can delegate subtasks to other specialized agents, you get a system that can handle complexity that would exhaust any single model.

At PrismGraph, we build what we call agent graphs — directed networks where agents are nodes and data/task handoffs are edges. A typical system might have:

  • A planner agent that decomposes the top-level goal
  • Researcher agents that gather information in parallel
  • A synthesis agent that combines findings
  • A critic agent that checks the output for errors and bias
  • An executor agent that takes the final approved action

Each agent is specialized. Each has its own tools, memory, and prompting strategy. The orchestrator ensures they work in concert without deadlocking or duplicating effort.

# Simplified example of agent delegation
async def analyze_market(company: str) -> Report:
    plan = await planner.create_plan(f"Analyze {company}")
    
    data = await asyncio.gather(
        researcher.fetch_financials(company),
        researcher.fetch_news(company),
        researcher.fetch_competitors(company),
    )
    
    draft = await synthesizer.write_report(plan, data)
    critique = await critic.evaluate(draft)
    
    if critique.has_issues:
        draft = await synthesizer.revise(draft, critique.feedback)
    
    return draft

The Hard Problems

Building agentic systems is harder than it looks. The main challenges we've encountered:

Prompt brittleness: Agents that work great in testing fail in production because the real world doesn't follow the script. We address this with adversarial testing and graceful degradation.

Tool call failures: Tools fail. APIs rate limit. Databases time out. A robust agentic system needs retry logic, fallback tools, and the ability to acknowledge what it doesn't know.

Context window exhaustion: Long-running agents accumulate context. We use hierarchical summarization and selective retrieval to keep agents focused.

Observability: When a multi-agent system produces a bad result, which agent is to blame? We build extensive tracing into every system we ship — every agent action, every tool call, every handoff is logged with full context.

What This Means for Your Business

If your team is spending significant time on repetitive analytical work — market research, data cleaning, report generation, customer support triage, compliance checking — there's a high probability that an agentic AI system can automate 70-90% of it.

The infrastructure to build these systems reliably now exists. The remaining barrier is mostly organizational: knowing what problems to target, how to evaluate success, and how to integrate AI-driven outputs into human decision-making workflows.

That's exactly the problem we help companies solve at PrismGraph.

Jordan Lee is Head of AI Research at PrismGraph Technologies. Previously at DeepMind, specializing in multi-agent reinforcement learning.

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