Beyond the Chat Metaphor
A standard chat completion consumes a prompt and returns text. An agentic runtime loops: interpret goal, optionally explain reasoning, select a tool, pass inputs, read observation, continue or finish. Frameworks implement this pattern with varying sophistication; the architectural implication is constant — each loop is a decision with side effects.
ReAct showed that grounding actions in external retrieval can reduce hallucinated reasoning on knowledge tasks. It also showed that traces matter: thought-action-observation sequences are easier to diagnose than opaque outputs. Enterprises should require similar inspectability in production agents — not necessarily verbatim chain-of-thought publication to end users, but retained internal traces for review.
First Decisions — Before Vendor Selection
Define the business outcome and loss tolerance. What decision or task are you automating? What is the worst credible failure — financial, regulatory, customer, operational? Loss tolerance sets authority tier.
Name systems the agent may read or change. Read access to CRM differs from write access to billing. Document each integration, data classification, and approval level. Temporary broad access during pilots has a habit of becoming permanent.
Set action limits, approvals, and escalation paths. Which actions are auto, which require human approval, which are forbidden? Encode in policy and enforce in tool configuration — not only in prompt instructions models sometimes ignore.
Test failure modes before production access. Wrong tool selected. Stale context. Overstepped authority. Partial execution leaving inconsistent state. Red-team these deliberately.
Measure value, cost, quality, and control together. Actions taken, escalations, overrides, errors caught in review, token and infrastructure cost. Value without control metrics invites scaling problems; control without value metrics invites cancellation.
Authority Tiers — A Practical Starting Point
Most enterprises should begin conservatively:
- Read and summarize — no external writes; proves retrieval and reasoning value.
- Draft for approval — creates artifacts humans send or commit.
- Execute within limits — small, reversible actions with logging.
- Broader autonomy — only after evidence from lower tiers and explicit executive approval.
Skipping tiers because a competitor announced "full agents" trades schedule for incident probability.
Owners, Not Committees
Someone must pause the agent, revoke a tool, explain an incident to leadership, and approve boundary changes. A shared Slack channel is not ownership. The owner should be senior enough to enforce pause during an investigation and accountable enough to report outcomes.
Key Takeaways
- Agentic LLMs combine reasoning and tool use; each loop can change external state — treat that as authority delegation.
- ReAct-style traceability is a design requirement for production diagnosis and audit, not a research curiosity.
- Outcome, systems, limits, failure testing, and named ownership precede platform choice.
Strategic Recommendations
- Publish an agent authority matrix aligned to data classification and materiality policy.
- Default new agents to read-only or draft-only until evaluation data supports write tiers.
- Retain internal thought-action-observation or equivalent logs for material workflows.
Next Steps
- Freeze new write-tool integrations until each has documented authority and owner.
- Conduct a failure-mode exercise on your highest-access pilot agent.
- Report overrides and incidents alongside productivity metrics at the next leadership review.
