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Why Your Fun AI Experiment Could Become Your Most Expensive Colleague

Written by Tony Wood | Jan 27, 2026 11:49:38 AM

Agentic AI has moved from clever demos to systems that can take action, and leadership teams now have to decide what is allowed to act, where, and under whose name.

Strategic Imperative

Open-source agent frameworks are suddenly good enough to feel irresistible. You can prototype in days, wire tools together, and watch an agent complete a workflow end to end.

Here’s the thing. The technical leap is not the hard part any more. The hard part is operational: permissions, oversight, auditability, and the human impact of letting software act with intent.

If you want a simple leadership lens for 2026, it is this:

  • Experimentation is a competitive advantage
  • Ungoverned autonomy is operational debt
  • Personalities and permissions are an operating model, not a gimmick

2025 marked the real arrival of AI agents: They moved beyond chat to autonomous action, using tools, coordinating workflows, and executing tasks across systems. Open standards and agentic platforms accelerated adoption, turning AI agents into practical enterprise infrastructure.

❓ The challenge for 2026: Governance. Security risks, workforce impacts, energy demands, and unclear regulations are now front and center. The next phase won’t be about smarter agents alone, but about deploying them safely, responsibly, and at scale.

Execution and oversight will determine who captures value.
https://www.linkedin.com/posts/josh-tseng_ai-agents-arrived-in-2025-heres-what-happened-activity-7414656568136429568-Yx5j

 

What ClawdBot Gets Right

ClawdBot is exciting because it leans into the part most organisations secretly need: a place to play, test, and learn in the open.

The best bits of the idea are leadership friendly:

  • Fun experimentation
    • You need a sandbox where people can try agentic workflows without months of committees.
  • A single backbone that makes roles legible
    • Think in terms of: an individual agent, the actions it can take, and the skills it has earned.
  • Personality files as a practical control surface
    • Not personality for vibes.
    • Personality for repeatable behaviour, boundaries, and decision style.
  • A bias towards swarms
    • One agent is brittle.
    • A small group of specialised agents can cross-check, challenge, and hand off.

If you have not looked at how fast the ecosystem has matured, it is worth scanning what is now available and how different frameworks make different trade-offs.

Open-source agent frameworks are rapidly maturing, offering developers a spectrum from low-code simplicity to enterprise-scale robustness. We’ve seen how LangChain (with LangGraph), AG2, Google’s ADK, and CrewAI each take a distinct approach: from modular chains to conversational agents, from graph-based flows to role-based “crews.” The best framework for you depends on your context. A lone developer building a smart assistant might favor community-supported tools like LangChain, while a Fortune 500 team orchestrating AI workflows could opt for ADK or CrewAI to meet security and scalability demands. What’s clear is that agentic AI is here to stay – and adopting one of these frameworks can accelerate your journey.
https://www.linkedin.com/pulse/open-source-agent-frameworks-showdown-2025-langchain-ag2-gaddam-d51te

The Hidden Risk (The Fun Drunk Relative Problem)

I love the energy of open experimentation. It is where the breakthroughs happen.

But leadership needs to recognise the pattern:

  • In week one, the agent feels like a fun experiment.
  • In week three, it feels like a crazy employee who is productive but unpredictable.
  • In quarter two, it can become the fun drunk relative: entertaining short term, expensive clean-up later.

That cost rarely shows up as one dramatic failure. It shows up as a steady drip:

  • Confidential data ends up in the wrong place
  • An agent takes an irreversible action with the wrong assumptions
  • Nobody can explain why a decision happened
  • Staff lose trust because the system behaves inconsistently
  • Risk teams are asked to approve something that has no audit trail

So the question is not, "Should we experiment?"

The question is, "How do we stop experiments becoming production by accident?"

How Leaders Should Think About Personalities As Guardrails

Most governance conversations start with policy. That matters, but it is not enough.

With agents, you are not only deploying software. You are delegating judgement.

That is why personality files and profiles are useful. They let you encode:

  • Decision style (cautious vs fast)
  • What the agent refuses to do
  • How it escalates to a human
  • What it logs
  • Which tools it can touch
  • Which data it is allowed to read or write

This is where the analogy becomes practical:

  • You do not want a creative person doing your taxes
  • You want a diligent, process-driven person doing the accounts
  • Then you want the creative person back where creativity pays

In agent terms:

  • Put your creative agent in ideation, drafting, and exploration
  • Put your diligent agent in reconciliation, approvals, and checks
  • Make handoffs explicit, logged, and reviewable

If you want a concrete starting point for personality and workflow files, AGENTS.md is a useful pattern to learn from and adapt.

Swarms Are Not A Party Trick, They Are A Safety Feature

A well-designed swarm is not "more autonomy". It is better division of labour.

A leadership-level way to describe it:

  • One agent proposes
  • Another agent challenges
  • Another agent checks compliance and privacy
  • Another agent writes the final output in the approved format
  • A human signs off on anything high impact

That structure can reduce single-agent overconfidence and create a built-in review loop.

This emerging field, known as multi-agent AI or Swarm AI, mirrors the collective strategies seen in nature. Just as ants optimise entire colonies without central leadership and bees coordinate complex foraging patterns through simple signals, AI systems are learning to collaborate, compete, challenge, and refine each other in real time. This evolution represents a profound shift in how intelligence is designed and deployed.

Multi-agent AI breaks that limitation by distributing intelligence across many smaller agents, each with its own objective, skill, or perspective. These agents can specialise, one focusing on anomaly detection, another on forecasting, another on risk scoring and then share or contest information with each other. What emerges is not the opinion of one model but a conversation among models.
https://www.linkedin.com/pulse/when-models-go-multi-agent-rise-swarm-ai-iain-brown-phd-ij7ge

A Simple Governance Model That Does Not Kill The Fun

If you want to move fast without becoming reckless, keep it boring and explicit.

Use these principles as your minimum bar for any agent that can take actions:

  • Non-harm and protection
    • No high impact actions without a human approval gate.
  • Kindness, dignity, and respect
    • No automated outputs in sensitive people contexts without review.
  • Honesty and transparency
    • Logs, traceability, and clear user disclosure where relevant.
  • Privacy and confidentiality
    • Least privilege access, data minimisation, strict retention.
  • Lawful compliance and accountability
    • A named executive owner for each production agent.

Then convert that into operating practice:

  • A permission matrix per agent (tools, data, environments)
  • A "stop button" and rollback plan
  • A lightweight change process for personality file updates
  • Regular reviews of incidents, near misses, and unexpected behaviours

What To Do Next (24 Hours And Next Weeks)

In The Next 24 Hours

  • Pick one workflow that is useful but low risk (read-only where possible).
  • Decide what the agent can touch:
    • Data
    • Tools
    • Systems
  • Write a one-page behaviour contract:
    • What it must do
    • What it must never do
    • When it escalates to a human
  • Turn on logging from day one, even in pilots.

In The Next 4 Weeks

  • Build a small swarm, not a hero agent:
    • Proposer
    • Checker
    • Compliance and privacy reviewer
  • Create 2 personality profiles:
    • Creative explorer
    • Diligent accountant
  • Run a simple red-team exercise:
    • What happens if prompts are malicious?
    • What happens if data is wrong?
    • What happens if a tool call fails?
  • Decide what "production ready" means in your organisation:
    • Named owner
    • Audit trail
    • Approval gates
    • Incident playbook

Call to Action

Call to Action: Pick one workflow that matters, then define the agent’s role, tools, and refusal rules in writing. In the next 24 hours, ship a logged, low-risk pilot. In the next weeks, add a checker agent, tighten permissions, and make human sign-off the default for high-impact actions.

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