Stop treating agentic AI like traditional software
Most generative AI pilots never see daylight. An MIT study put the failure rate at 95%. Agentic systems aren't immune, but the teams that ship are making one crucial change: they've flipped the software lifecycle. Less time "crafting" agents, far more time testing and governing them.
Traditional apps were handwritten and deterministic. Agents are probabilistic, tool-using, and context-sensitive. If you still spend 80% of your time on design/build, you're under-investing in the exact place agents break: evals, guardrails, and production feedback loops.
What the 5% do differently
- They build evaluations first, not last. Quality gates decide what ships.
- They narrow scope. One agent, one job, clear boundaries.
- They treat security and policy as part of the design, not an afterthought.
- They use supervisor agents to inspect, coordinate, and enforce outcomes.
- They move from one chatbot to multi-agent workflows with clear contracts.
Evals are your new CI/CD
Teams that leaned into evaluations put nearly six times more projects into production, according to Databricks' data. That's not luck. It's a process shift.
- Define pass/fail assertions: factual grounding, policy checks, safety, tool-use success, latency, cost.
- Curate golden datasets and augment with synthetic edge cases. Use SMEs to label what "good" looks like.
- Run regression suites on every model, prompt, tool, and retrieval change. Block merges on quality drops.
- Start in shadow mode. Compare agent output to human outcomes before you touch production traffic.
- Canary release. Roll out to 1-5% with live evals and auto-rollbacks if metrics slip.
- Instrument feedback loops. Capture user ratings, escalation rates, override reasons, and post-action audits.
Narrow the scope to win early
Agents aren't catch-all click-and-go widgets. They shine on specific, repeatable workflows with clear data sources and clear outcomes. Think onboarding checklists, policy-compliant document generation, or support triage with defined playbooks.
Customer support is a standout. Research last year projected agents will handle more than two-thirds of support interactions by 2028. Focus them on triage, knowledge lookup, and action sequencing-then escalate cleanly to humans.
Multi-agent workflows: specialists with a supervisor
Enterprises are shifting to multi-agent setups. Instead of one generalist bot, you wire up specialists-each with a narrow mandate-and a supervisor that calls the shots. It's like a home renovation: bring in the plumber, the renderer, the window fitter. One foreman ensures the work fits together.
- Information Extraction agents (popular across deployments) mine structured and unstructured data to produce clean, typed facts.
- Knowledge Assistant agents retrieve, ground, and draft responses from approved sources.
- Supervisor agents plan steps, assign tasks, validate outputs, and enforce policy.
Information extraction is especially impactful for enterprises drowning in documents. Pulling consistent facts from PDFs, emails, and tickets unlocks workflows that were manual or brittle with rules.
Supervisor agents: guardrails that scale
Hallucinations and rogue actions aren't just PR risks; they're blockers to production. Supervisor agents reduce that risk by validating each step and outcome against policy and expected results. Think of them as continuous code reviews for agent work:
- Work agent produces. Inspector checks. Supervisor enforces outcome and policy alignment.
- Policies as code: allow/deny lists for tools, data scopes per task, rate limits, and approvals.
- Pre-commit checks: verify citations, tool outputs, and safety before any external action runs.
- Human-in-the-loop thresholds for high-impact decisions and ambiguous cases.
Analysts expect "guardian" agents to make up a noticeable share of deployments by 2030. The pattern is simple and effective: validate first, act second.
Governance that actually ships
Companies with stronger AI governance put more than 12 times more projects into production in Databricks' reporting. But governance across multiple departments is hard. Make it practical and operational, not a binder on a shelf.
- Data contracts for retrieval and tools: schema, PII rules, freshness, and quality SLOs.
- Model and prompt registry with versioning, approvals, and change logs.
- Capability passports per agent: what it can access, what it can do, and where it can act.
- Audit trails for every tool call, decision, and override-immutable and queryable.
- Incident playbooks for hallucinations, data leakage, and policy breaches.
- Risk mapping to compliance frameworks with DPIAs where needed.
Pilot-to-production playbook
- Choose a narrow workflow with clear KPIs (e.g., first-contact resolution, handle time, defect rate).
- Start with a small team of specialists plus a supervisor. Lock tools and data to least privilege.
- Build the eval suite before the UI. Set acceptance thresholds and failure modes.
- Run in shadow mode for two weeks. Compare outcomes and costs against the baseline.
- Canary to 5% of real traffic. Gate expansion on eval pass rates and incident counts.
- Scale gradually. Add new tools and contexts only after new evals and policy checks land.
Team and skills you'll need
- Agent architect to design workflows, interfaces, and tool boundaries.
- Eval engineer to build datasets, metrics, and CI gates.
- Safety/red team to probe for jailbreaks, leakage, and risky actions.
- Data product owner to manage sources, contracts, and quality.
- LLMOps/MLOps to run registries, telemetry, and rollouts.
- SecOps to enforce identity, secrets, and action scopes.
If you're leveling up your staff for evals, supervision patterns, and LLMOps, explore practical training by job role here: Complete AI Training - Courses by Job.
The takeaway
Stop treating agents like traditional apps. Shrink the build phase, expand testing and governance, and ship with supervisors and clear scopes. Pick practical use-cases, wire in evals as CI, and let data-not hype-decide what moves to production.
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