FROM LEGACY TO AGENTIC
Most regulated companies run on RPA, manual review, and spreadsheets designed before LLMs existed. The shift to agentic ops doesn't have to be a rip-and-replace.

WHY NOW IS THE MOMENT
Legacy automation was designed for predictable, rules-based processes. It breaks on exceptions, it doesn't adjust, and it was never made to work alongside AI agents. The good news: your team doesn't need to throw it away. The shift to agentic ops is about adding intelligence, context, and judgment to the infrastructure you already run, at a pace your compliance team can stay ahead of.
WHY THE SHIFT IS HAPPENING NOW
01
Models are ready
Today's models reason about complex compliance scenarios, draft regulatory filings, and handle multi-step workflows with genuine judgment, not just pattern matching.
02
Standards are emerging
MCP (Model Context Protocol) is becoming the standard for how AI agents connect to tools and data. The infrastructure question that blocked enterprise adoption a year ago has a clear answer.
LEGACY OPS VS AGENTIC OPS
Same processes. Different outcomes. What actually changes when you move.

Q1
How do I migrate from legacy ops to agentic ops?
Start with one workflow. Compound from there. Your team doesn't go from manual to fully autonomous overnight, and shouldn't. Pick one workflow where the use case is clear and the risk is contained. Let your AI agents assist before they act. Expand their scope as your team gains confidence, calibrates escalation thresholds, and accumulates audit history. With Forest, you control the pace and regulation sets the ceiling.

Q2
What should I look for in agentic ops infrastructure?
Not all agentic infrastructure was designed for regulated industries. Four things matter. Your data stays in your environment. Every action is audited at the record level. Permissions scope individual AI agents to individual actions, workflows, and records. You're not locked into one model or vendor. If a platform can't give you all four, it wasn't designed for your industry.
Let us get you started
Frequently asked questions
You still have question ?
Book a conversation with a forest expert
How do I migrate from legacy ops to agentic ops?
Start with one workflow. Compound from there. Pick one where the use case is clear and the risk is contained. Let your AI agents assist before they act. Expand their scope as your team gains confidence, calibrates escalation thresholds, and accumulates audit history.
Do I need to replace my RPA when moving to agentic ops?
No. Legacy automation runs the predictable steps well. Agentic ops adds intelligence for exceptions and judgment calls. On Forest, your existing RPA continues to run and AI agents pick up where the rules engine hits its limits.
How long does it take to move a workflow to agentic ops?
Time-to-production depends on your data readiness and workflow complexity. First workflows on Forest typically run in weeks, not quarters. Subsequent workflows compound faster as your team accumulates audit history and calibrates escalation.
What should I look for in agentic ops infrastructure?
Four things. Your data stays in your environment. Every action is audited at the record level. Permissions scope individual AI agents to individual actions, workflows, and records. You're not locked into one model or vendor. If a platform can't give you all four, it wasn't designed for regulated industries.
Does DORA require agentic ops governance?
DORA has been in force since 17 January 2025 and sets operational resilience obligations for ICT systems in financial services, including AI. Every AI agent action needs to be explainable, auditable, and reversible. Forest's record-level audit and scoped permissions were built for this.
What does the EU AI Act require for agentic ops?
For high-risk AI systems, the EU AI Act requires risk management, data governance, technical documentation, human oversight, and audit trails. Obligations currently apply from 2 August 2026, with a proposed delay to 2 December 2027 under EU discussion. Forest's design (record-level audit, human-in-the-loop, scoped permissions) maps directly to these requirements.
Which workflow should I start with?
Pick one where the use case is clear and the risk is contained. KYC exception review, dispute triage, onboarding follow-up. Workflows where headcount is high and rules engines never worked well are the best first candidates.
What is Model Context Protocol (MCP)?
Model Context Protocol is the open standard, released by Anthropic in late 2024, for how AI agents connect to tools and data. Forest exposes your data and tools through the Forest MCP Server, which any MCP-compatible AI agent can call under the same permissions and audit coverage as your team.
How do I calibrate AI agent escalation thresholds?
Escalation thresholds are configured by confidence level, action type, and risk score. Your team sets them per workflow. Forest routes the right context to the right human at the right moment, with the case history attached. Adjust as your team gains confidence and audit history accumulates.
Can I switch AI agent vendors after deployment?
Yes. Forest is model-agnostic and framework-agnostic. Any MCP-compatible runtime works. Switch as the landscape evolves. Your workflows and audit history stay intact.
