Agentic AI-as-a-Service¶

Autonomous AI agents for back-office automation — multi-step reasoning, tool use, long-horizon tasks.

What "agentic" means¶

Beyond single-turn LLM completion: - Plan multi-step tasks - Call tools, observe results, plan next step - Persist state across turns - Recover from errors - Escalate when stuck

Typical use cases¶

• Loan application processing (gather docs → verify → score → recommend)
• Insurance claims (review → assess → approve/escalate)
• IT operations (diagnose alert → look up runbook → execute fix → verify)
• Procurement (sourcing → quote comparison → recommendation)

Not chat — task automation.

IAM¶

Role	Can do
agent.viewer	View agents, task logs
agent.designer	Define agents, tools, workflows
agent.deployer	Deploy agents to production
agent.admin	Manage system, audit

Agent definition¶

yaml agent: name: loan-application-processor model: llama-3.1-70b-instruct tools: - lookup_customer - fetch_credit_report - calculate_eligibility - send_notification max_steps: 20 escalation_criteria: "credit_score < 600 OR loan_amount > 5000000"

Safety¶

Agents acting autonomously are powerful — and risky: - Bounded action set (only configured tools) - Step limit (max-steps prevents runaway) - Escalation criteria (when to involve humans) - Audit log per agent run - Sandboxing (agent can't access arbitrary resources)

Don't grant agents permissions you wouldn't give to a junior employee.

Related¶

• Agentic AI — Operation
• Agentic AI — Troubleshooting
• LLM-as-a-Service

Metrics¶

Metric	Healthy	Alert
agent.tasks_per_hour	matches input rate
agent.completion_rate	> 80%	< 60%
agent.escalation_rate	10-25%	> 40% (agent too weak)
agent.average_steps	< 10	> 15 (going in circles)
agent.cost_bdt.per_task	within budget	climbing
agent.tool_errors_per_hour	low	spikes

Task lifecycle¶

Input → Plan → Execute → Observe → Next? → Done or escalate

Each step: 1. Agent prompts LLM with current state 2. LLM decides: call tool, ask user, or finish 3. Tool/result observed 4. Loop until done or escalation

Tool registry¶

Agents only have access to registered tools:

bash cd agent tool register --name lookup_customer \ --endpoint https://internal.acme.com/customers/:id \ --schema @lookup_customer_schema.json \ --auth bearer:acme-internal-token

Cost monitoring¶

Per-task cost depends on: - Number of LLM calls (steps) - Token volume per call - Tool execution cost

bash cd agent cost report --agent loan-processor --since 24h

Outlier expensive tasks usually loop unnecessarily — tune planning.

Human-in-the-loop¶

For uncertain decisions:

yaml agent: escalation_criteria: "uncertainty > 0.5" human_review_required: - loan_amount > 1000000 - first_time_applicant - score_borderline (550-650)

Escalations route to human queue; humans approve / reject / send back to agent with feedback.

Audit¶

Per task: - Full step-by-step trace - Each tool input/output - LLM prompts and responses - Final decision

Auditable for compliance (regulated industries) and debugging.

Related¶

• Agentic AI — Administration
• Agentic AI — Troubleshooting

Agent loops¶

agent.average_steps > 15:

• Agent stuck in retry loop on same tool
• Planning logic failing to converge
• Tool returning ambiguous results

Inspect trace; usually obvious pattern. Common fixes: - Tool returns clearer signal (success/failure/retry) - Agent prompt directs different tool after N failures - Hard step cap forces escalation

Tool call failures¶

agent.tool_errors_per_hour spike:

• Backend service down (transient)
• Auth credentials expired (refresh)
• Schema drift (tool changed; agent expectations didn't)

For transient: agent should retry. For persistent: escalate.

Hallucinated tool calls¶

Agent calls a tool that doesn't exist or with wrong parameters: - System prompt clearer about available tools - Lower temperature for tool-call decisions - Tool registry validation rejects invalid calls

Escalation queue piling up¶

Humans can't keep up with escalations: - Reduce escalation criteria (handle more autonomously) - Improve agent quality (fewer false escalations) - Add reviewers / shifts

Don't lower criteria to relieve queue if agent's decisions are actually borderline.

Cost runaway¶

agent.cost_bdt.per_task climbing: - Step count rising (loops) - Tool calls expensive (one calls another that calls another) - Long context per step

Tune; cap per-task budget:

bash cd agent budget set --agent loan-processor --max-bdt-per-task 50

Agent decision audited as wrong¶

Compliance / audit reviews and disagrees with agent decision: - Inspect trace; was logic sound given inputs? - If inputs wrong: data quality issue - If logic wrong: tune agent prompt or escalation criteria - Document; learn

New regulation requires agent change¶

Regulator updates rules; agent's logic needs update: - Update prompt / tools - Test in shadow mode (agent runs but doesn't act) - Switch to production after validation

Plan for regulatory change as ongoing cost.

Related¶

• Agentic AI — Administration
• Agentic AI — Operation