Dr. Charalambos Theodorou
AI Researcher / Engineer | Machine Learning Expert | Entrepreneur | Investor
Talk-style reflection, February 5, 2026

Fresh reports this week (Celonis 2026 Process Optimization Report, DigitalOcean Currents) paint a clear picture: Enterprises are aggressively chasing agentic AI, 85% aim to become "agentic enterprises" within 3 years, 90% already exploring multi-agent systems for complex decisions, but most hit a wall when moving from pilots to production.

Key gaps:
- Operational context & process readiness: Agents need grounded data and clean processes to execute reliably. Without it, they amplify inefficiencies (Celonis: majority lack this foundation).
- Inference over training spend: DigitalOcean notes the shift, inference costs now dominate as agents run in production, yet many orgs aren't optimized for scale.
- Governance & security lag: Sprawl risks, over-privileged identities, prompt injection, incidents like Moltbook's quick exposure remind us runtime safeguards are non-negotiable.
- Human-in-the-loop as default guardrail: Most companies rely on HITL, but rising costs push for more autonomy, the tension is real.

From leading production multi-agent teams (shipping aligned systems with real ROI: cost savings, 30% faster deployments, proactive safety via sim/red-teaming):

What Actually Works in Production (Lessons from the Trenches)

  1. Data-native context first — Tools like Snowflake's new Cortex Code (launched Feb 3) show the path: Agents that deeply understand enterprise data context automate pipelines, analytics, ML — without constant human fixes.
  2. Orchestration & standards — MCP-like protocols, LangGraph evolutions, control planes turn agents into reliable "digital employees", goal-oriented, auditable, hybrid.
  3. Runtime safety as architecture — Constitutional flags, provenance logging, proactive adversarial sim, zero-trust identity — embed from day one to prevent drift/cascades.
  4. ROI measurement & workforce shift — Tie agents to metrics (decision latency, cost reduction). Train teams on orchestration over prompting, every employee becomes a supervisor.

2026 Outlook

The divide widens: Early adopters operationalize agents for double-digit efficiency gains; laggards stay in pilots, facing regret from ungoverned sprawl.
Prediction: By mid-year, governance/ops maturity becomes the primary differentiator, not model sophistication.

What's your biggest barrier to agentic production right now, data readiness, governance, scaling costs, or something else? Share in comments or on X, let's compare real-world notes.

Stay engineering responsibly (and grounded in production).