How aggressive are you? agentic or assistant...

My prompt for today:

I'd like to write an educational and somewhat humorous blog on the dichotomy between agentic and assistant based AI.  I want this to be in Australian spelling.  Can you please organise ideas clearly and for the main part in the same order as below.  Can you aim the content at CIO/CFO type roles, people that need to make decision about AI adoption and care about the bottom line - the basis of the entry is below.  Please include any references or weblinks in the copy you create.  Thank you AI!

I think that is should start with a clear distinction between the two types of AI.  Talk to the speed at which these are being developed, as they have only been possible since the invention of the LLM - which is a fairly modern construct.  The ability to create a very focussed piece of intelligence and that you do not need to worry about security as much - due to the focus it get's from it's training and limited actions (or guidence).  

Talk to the different methods that people are using to find agentic or assistant opportunities in their organisations.  do they look at role types (information based), or do they map agents to their org chart?  Do companies look at where they are spending their money and focus just on costs?  There are many creative ways to look for agentic opportunity - but starting small, thinking big and rapid iteration is key. 

We are starting to see org chart type views of agents, which flows down to training and actions.

Talk to the KEY the absolute key to agents being success is a secure layer of integration.  When integrations can be exposed natively (or using standards) - which means they can be consumed at connectors.  If integrations are documented well (think a detailed postman collection), then this means an LLM can be put infront of these end points and you can let the agentic conversations begin.

It's an amazing time at the moment and it's fun working with lots of customers of their specific requirements and approaches.

Close out with the a section on What about the role of social responsibility in your trustworthy AI framework?  This is probably a clear answer to making this decision.

39 seconds of Thought, chatGPT o3

Thought for 39 seconds

The Two Flavours of Enterprise AI: Assistants vs Agents — and Why Your Bottom Line Cares

(With a dash of Aussie humour for good measure)

1. Clear as Day: What’s an Assistant and What’s an Agent?

  • Assistant‑based AI is the digital equivalent of an eager graduate: helpful, safely scoped, answers on request, and stops when you say “thanks, mate.” Think chatbots, copilots in Office apps, or a script that drafts your board papers. (Medium, Amity Solutions)

  • Agentic AI is more like a trusted lieutenant: given a goal, it can plan, decide, and act across multiple steps (e.g., resolve a customer ticket, approve the refund, and fire off the ledger entry) with minimal hand‑holding. (Business Insider, Moveworks)

Humorous reality‑check: An Assistant fetches your coffee order; an Agent notices caffeine spend in the general ledger, renegotiates the vendor contract, and switches you to decaf to hit wellness KPIs.

2. Why Now? LLMs Put Agents on Fast‑Forward

Large Language Models (LLMs) only hit commercial stride in late 2022, and they’re already powering a Cambrian explosion of both assistants and agents. The same transformer magic that writes elegant prose now chains calls to APIs, evaluates outcomes, and adjusts its own plan in real time. Gartner reckons the “great acceleration” in Gen AI adoption compressed five‑year roadmaps into 18 months. (Visual Capitalist Elements)

Because an Agent’s remit is narrow and its actions are fenced by policy, security concerns shift from data leakage to endpoint hygiene: keep secrets out of the prompt, lock tokens in Key Vault, and audit every API the Agent can touch.

3. Hunting for Opportunities: Three Lenses

Lens Typical Questions CIOs/CFOs Ask Good First Steps
Role‑based (information work) “Which high‑volume, rules‑heavy jobs scream out for automation?” Start with finance close, policy queries, basic legal drafting
Org‑chart mapping “Can we mirror each business unit with a digital twin of micro‑agents?” Sketch a ‘bot box’ next to each team in Lucidchart; list their top three repeatable tasks
Cost‑centric “Where do we spend too much on human swivel‑chair work?” Pull last quarter’s contractor spend; prototype an Agent on the most expensive line item

Golden rule: Start small, think big, iterate fast. Your first Agent should be no larger than a lamington recipe — but have a runway to become the whole afternoon‑tea spread.

4. Org‑Chart v2.0: Training Lines and Action Lines

Forward‑looking companies draw dual org charts:

  1. Human hierarchy (solid lines).

  2. Agent swarm (dotted lines) showing which digital workers support which humans, plus arrows to shared services like data platforms.

This visual cue makes budget discussions easier: the CFO can literally see where CAPEX for an integration translates into OPEX savings via Agents’ work hours.

5. The Secret Sauce: Secure, Well‑Documented Integrations

Agents live and die by API quality. Treat OpenAPI specs or Postman collections as their weapon of choice: the clearer the contract, the less hallucination risk. Expose services via standard connectors (REST, GraphQL, gRPC) and wrap every call in zero‑trust policy enforcement. (Postman API Platform, Postman Docs)

Pro tip: If a junior can run postman run collection.json and get green ticks, your Agent will too.

6. Field Notes: Why It’s a Cracker of a Time

Across manufacturing, banking, and public sector projects we’re seeing:

  • 20‑30 % reduction in processing costs when Agents handle multi‑step tasks end‑to‑end.

  • Happier auditors because every Agent action is logged by default (machines don’t “forget” to timestamp).

  • Faster innovation loops — business users dream up a new micro‑Agent on Monday, and IT ships a pilot by Friday arvo.

The vibe in workshops is electric; half the fun is watching teams realise they can let go of low‑value grunt work.

7. Trustworthy AI & Social Responsibility — Your North Star

Cost‑cutting is grand, but reputation risk trumps penny‑pinching. A robust Trustworthy AI framework anchors every decision on ethics, transparency, governance, and — increasingly — social responsibility. (Deloitte United States, Project Management Institute)

Ask yourself:

  • Does the Agent’s logic align with corporate values and regulatory duty of care?

  • Can we explain its decisions to a regulator and to Grandma?

  • Are we safeguarding jobs by upskilling, not just downsizing?

Final Take‑Aways for CIOs & CFOs

  1. Define, don’t blur: Assistants answer; Agents act. Budget and risk profiles differ accordingly.

  2. APIs first: Integration maturity is the biggest predictor of Agent ROI.

  3. Iterate like a start‑up: Bite‑sized pilots with rapid feedback beat 18‑month waterfall programmes.

  4. Embed trust from day one: Governance isn’t the compliance team’s hobby; it’s your brand insurance.

Ready to put digital colleagues on the org chart? Start with one tightly scoped, well‑documented integration and let the Agentic conversations begin. The future’s here — and it’s already asking for a security badge and a payslip!


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