From AI pilots to production: building agentic systems that perform.Presented by Edgeverve
Smart, semi‑autonomous AI agents handling complex, real‑time business work is a compelling vision. But moving from impressive pilots to production‑grade impact requires more than clever prompts or proof‑of‑concept demos. It takes clear goals, data‑driven workflows, and an enterprise platform that balances autonomy, governance, observability, and flexibility with hard guardrails from day one.
From pilots to the “operational grey zones”
The next wave of value sits in the connective tissue between applications — those operational grey zones where handoffs, reconciliations, approvals, and data lookups still rely on humans. Assigning agents to these paths means collapsing system boundaries, applying intelligence to context, and re‑imagining processes that were never formally automated. Many pilots stall because they start as lab experiments rather than outcome‑anchored designs tied to production systems, controls, and KPIs.
Start with outcomes, not algorithms. Translate organizational KPIs (cash‑flow, DSO, SLA adherence, compliance hit rates, MTTR, NPS, claims leakage, etc.) into agent goals, then cascade them into single‑agent and multi‑agent objectives. Only after goals are explicit should you select workflows and decompose tasks.
Pick targets, then decompose the work
What does “target” actually mean? In agentic programs, a target is a business outcome and the use case that moves it. For example, “reduce unapplied cash by 20%” target outcome; “cash application and exceptions handling” use case. With the use case in hand, perform persona‑level task decomposition: map the human role (e.g., cash applications analyst, facilities coordinator), enumerate their tasks, and identify which are ripe for agentification (data retrieval, matching, policy checks, decision proposals, transaction initiation).
Delivering on those tasks requires a data‑embedded workflow fabric that can read, write, and reason across enterprise systems while honoring permissions. Data must be AI‑ready, discoverable, governed, labeled where needed, augmented for retrieval (RAG), and policy‑protected for PII, PCI, and regulatory constraints.
Integration goes beyond APIs
APIs are one mode of integration, not the only one. Robust agent execution typically blends:
Stable APIs
with lifecycle management for core systems
Event‑driven triggers
(streams, webhooks, CDC) to react in real time
UI/RPA fallbacks
where APIs don’t exist
Search/RAG connectors
for documents and knowledge bases
Policy management
across tools and actions to enforce entitlements and segregation of duties
The north star is integration reliability — built on idempotency, retries, circuit-breakers, and standardized tool schemas — so agents don’t “hallucinate” actions the enterprise can’t verify.
A quick example: finance and facilities, in production
Inside our organization, we deployed specialized agents in a live CFO environment and in building maintenance. In finance, seven agents interacted with production systems and real accountability structures. Year‑one outcomes included: >3% monthly cash‑flow improvement, 50% productivity gain in affected workflows, 90% faster onboarding, a shift from account‑level handling to function‑level orchestration, and a $32M cash‑flow lift. These results don’t guarantee gains everywhere; they show that designing products can deliver measurable outcomes on a scale.
The four design pillars: Autonomy, governance, observability & evals, flexibility
1) Autonomy: right‑size it to the risk
Autonomy exists on a spectrum. Early efforts often automate well‑bounded tasks; others pursue research/analysis agents; increasingly, teams target mission‑critical transactional agents (payments, vendor onboarding, pricing changes). The rule: match autonomy to risk, and encode the operating mode suggest‑only, propose‑and‑approve, or execute‑with‑rollback per task.
2) Governance: guardrails by design, not as bolt‑ons
Unbounded agents create unacceptable risk. Build guardrails into the plan:
Policy & permissions: tie tools/actions to identity, scopes, and SoD rules.
Human‑in‑the‑loop (HITL): where mission‑critical thresholds are crossed (amount, vendor risk, regulatory exposure).
Agent lifecycle management: versioning, change control, regression gates, approval workflows, and sunsetting.
Third‑party agent orchestration: vet external agents like vendors, capabilities, scopes, logs, SLAs.
Incident and rollback: kill‑switches, safe‑mode, and compensating transactions. This is how you
scale innovation safely while protecting brand, compliance, and customers.
3) Observability & evaluations: trust comes from telemetry
Production agents need the same rigor as any core platform:
Telemetry: capture full execution traces across perception, planning, tool use, action supported by structured logs and replay.
Offline evals: cenario tests, red‑teaming, bias and safety checks, cost/performance benchmarks; baseline vs. challenger comparisons.
Online evals: shadow mode, A/B, canary releases, guardrail breach alerts, human feedback loops.
Explainability & auditability: why was an action taken, which data/tools were used, and who approved.
4) Flexibility: assume volatility, design for swap‑ability
Models, tools, and vendors change fast. Treat agentic capability as platform currency: create an environment where teams can evaluate, select, and swap models/tools without tearing down the build. Use a model router, tool registry, and contract‑first interfaces so upgrades are controlled experiments, not rewrites.
The agent platform fabric: how platformization turns goals into outcomes
A true agentic enterprise requires a platform fabric that transforms goals into outcomes, not a patchwork of isolated pilots. This platform anchors enterprise‑to‑agent KPI cascades, drives task decomposition and multi‑agent planning, and provides governed tooling and data access across APIs, RPA, search, and databases.
It centralizes knowledge and memory through RAG and vector stores, enforces enterprise controls via a policy engine, and manages performance and safety through a unified model layer. It supports robust orchestration of first‑ and third‑party agents with common context, embeds deep observability and evaluation pipelines, and applies disciplined release engineering from sandbox to GA. Finally, it ensures long‑term resilience through lifecycle management versioning, deprecation, incident playbooks, and auditable histories.
Guardrails in action: a BFSI example
Consider payments exception handling in banking — high stakes, regulated, and customer‑visible. An agent proposes a resolution (e.g., auto‑reconcile or escalate) only when:
The transaction falls below risk thresholds; above them, it triggers HITL approval.
All policy checks (KYC/AML, velocity, sanctions) pass.
Observability hooks record rationale, tools invoked, and data used.
Rollback/compensation is defined if downstream failures occur. This pattern generalizes to vendor onboarding, pricing overrides, or claims adjudication — mission‑critical work with explicit safety rails.
Scale beyond pilots
Scaling agentic AI beyond pilots demands disciplined readiness across nine fronts: leaders must clarify which KPIs matter and how agent goals ladder into them, determine which persona tasks are agentified versus remain human‑led, and align each with the right autonomy mode from suggest‑only to propose‑and‑approve to execute‑with‑rollback. They must embed governance guardrails, including HITL points and lifecycle controls; ensure robust observability and evaluation via telemetry, replay, audits, and offline/online tests; and verify data readiness, with governed, policy‑protected, retrieval‑augmented data flows. Integration must be reliable, with API lifecycle management, event triggers, and RPA/other fallbacks. The underlying platform should enable model swap‑ability and orchestration of first‑ and third‑party agents without rebuilding. Finally, measurement must focus on true operational impact cash flow, cycle times, quality, and risk reduction rather than task counts.
The takeaway
Agentic AI is not a shortcut; it’s a new system of work. Enterprises that approach it with platform discipline aligning autonomy with risk, embedding governance and observability, and designing for swap‑ability will convert pilots into production impact. Those that don’t keep accumulating impressive but disconnected demos. The difference isn’t how fast you ship an agent; it’s how deliberately you design the enterprise around it.
N. Shashidar is SVP & Global Head, Product Management at EdgeVerve.
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From flashy demos to practical impact, AI finds its real enterprise purposePresented by OutSystems
After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on the practical matters of governance, orchestration, and iteration, along with integrating agents into the systems they've spent decades building.
Enterprise leaders are increasingly focused on fundamentals. The priority is using new AI technologies
to accelerate productivity, improve delivery, and produce measurable business results.
Three elements shape this work:
The move from AI agent prototypes to agentic systems that deliver measurable ROI in production
The growing role of enterprise platforms in governing, orchestrating, and scaling AI agents safely
The rise of the generalist developer and enterprise architect as the most valuable technical profiles in an era of AI-generated code
Against this backdrop, the panel discussed governance frameworks, the economics of enterprise AI, and the limits of large language models without orchestration. The conversation ultimately turned to how leading organizations are building multi-agent systems grounded in existing enterprise data and workflows.
Agents in the real world
Enabling agents to work in production across the enterprise is best accomplished with a unified platform that handles development, iteration, and deployment. And that'swhere capabilities like the Agent Workbench in the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific. It provides the infrastructure to learn, iterate, and govern agents at scale.
His team at Thermo Fisher has moved away from single-task AI assistants in customer service to building a coordinated team of specialized agents using the workbench. When a support case arrives, a triage assistant classifies the request and dynamically routes it to the right specialist agent, whether that’s an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
"We don’t have to think about what will work and how. It’s all pre-built," he explained. "Each agent has a narrow role and clear guardrails. They stay accurate and auditable.”
Governing the risks of shadow AI
A new category of risk emerges when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, this is ungoverned shadow AI. These homegrown products are prone to hallucinations, data leakage, policy violations, model drift, and agents taking actions that were never formally approved.
To get ahead of the risk, leading organizations need to do three things, said Luis Blando, CPTO of OutSystems.
"Give users guardrails. They’re going to use AI whether you like it or not. Companies that seem to be getting ahead are using AI to govern AI across their full portfolio,” he explained. “That is the difference between shadow AI chaos and enterprise-grade scale.”
Eric Kavanagh, CEO of The Bloor Group, noted that governance requires a layered set of disciplines that includes securing data, monitoring models for drift, and making deliberate choices about where AI connects to existing business processes.
“Companies don’t have to be manually creating these controls," he added. "A lot of those guardrails and levers are baked in to platforms like OutSystems.”
Why the real orchestration challenge is models vs. platforms
Much of the early excitement around enterprise AI focused on selecting the right large language model. Now the harder challenge, and far more durable source of value, is orchestration. This includes routing tasks, coordinating workflows, governing execution, and integrating AI into existing enterprise systems.
Scott Finkle, VP of development at McConkey Auction Group, noted that LLMs, however impressive, are pieces of complex workflows, not final solutions. Organizations should be ready to hot-swap between Gemini, ChatGPT, Claude, and whatever emerges next without having to rebuild the agentic system around it.
A platform with orchestration capabilities makes that possible. It manages the lifecycle, provides visibility, and ensures processes execute reliably, even as AI handles the reasoning layer on top.
“The AI and the models change, the workflows can change, but the orchestration remains the same," Finkle said. "That’s how we’re going to extract value out of AI.”
The economics of enterprise AI investing
Security, compliance, governance, and platform-level AI capabilities will all command greater investment in 2026, particularly as AI moves into core workflows like finance and supply chain. Enterprises should favor incremental wins rather than expect big, immediate gains.
“We’re focusing on base hits," Finkle said. "The way it counts is by getting something into production and having it make an impact. Big investments in pilot projects that don’t make it into production don’t save any money. It’s not going to happen overnight, but over time I think we’ll see tremendous savings.”
There's still a split in how enterprises are approaching AI transformation. Some start from scratch and reimagine every process. Others, especially those with billions of dollars in existing infrastructure depreciating in-house, want AI to integrate with their systems. They want agentic systems to reuse data, APIs, and proven processes while speeding up delivery. The agent platform approach serves both camps, but particularly the latter. Organizations can deploy agents where they add clear value while preserving the integrity of established, deterministic workflows.
The rise of the enterprise architect and the generalist developer
As AI accelerates code generation, bottlenecks in software delivery are dissolving. In its place is a premium on systems thinking. This is the ability to understand the broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Kavanagh pointed to enterprise architects specifically as the professionals best positioned to capitalize on this moment.
“We’re entering a very interesting age of the generalist," he explained. "The better you know your enterprise architecture and your business architecture and how those things align, the better off you’re going to be. ”
“The result is faster delivery with fewer interruptions and fewer bugs," Kavanaugh said. "You can focus on the non-repetitive tasks. It’s a benefit to the developer, to the business, and to the whole IT organization.”
Catch the entire webinar here.
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Workflow control planes bring order to chaotic enterprise agent systemsEnterprise AI teams are hitting a wall — not because their models can't reason, but because the workflows underneath them were never built for agents. Tasks fail, handoffs break, and the problem compounds as organizations push agents deeper into back-office systems. A new architectural layer is emerging to address it: workflow execution control planes that impose deterministic structure on processes agents are expected to run.
One of the companies bringing this to the forefront is Salesforce, with a new workflow platform that turns back-office workflows into a set of tasks for specialized agents to complete. Users can upload their processes or use one of the set Blueprints provided by Salesforce, and Agentforce Operations will break it down for agents.
Salesforce senior vice president of Product, Sanjna Parulekar, told VentureBeat in an interview that the problem is that many enterprise workflows are not built for agents. “What we’ve observed with customers is that a lot of times, the brokenness in a process is probably in your product requirements document,” Parulekar said. “So when that’s uploaded into a product, it doesn’t quite work. We can optimize it and cut out some things and replace it with an agent.”
Without this control panel layer, enterprises could risk deploying agents that increase cost rather than fix their workflow problems.
Making the workflow work for agents, not just humans
Enterprises deploying agents are learning a costly lesson: Their workflows were designed around human judgment gaps, not machine execution. Processes that evolved through years of workarounds — loosely defined steps, implicit decisions, coordination that depends on individuals knowing what to do next — break when agents are asked to follow them literally.
Even with all of an enterprise’s context at its fingertips, AI systems will have difficulty completing tasks if it is not clear what it’s supposed to do.
Parulekar said her team found that focusing on what makes the process tick and breaking it down into more explicit steps and workflows makes the system more deterministic. Then, when platforms like Agentforce Operations introduce agents, those agents already know their specific tasks.
“It forces companies to rethink their processes and introduces observability into the mix because of the session tracing model in the system,” she said.
Parulekar said human checks can be built into the system, so the process is more transparent.
What makes this approach different from other workflow automation offerings is that it doesn’t rely on agents to decide what to do next; the system does. Unlike more traditional automation tools that route tasks and agents on probabilistic decision-making, this enforces execution on a more pre-defined, deterministic structure.
The problem it introduces
Codifying a workflow doesn't fix a broken one. If a process has flawed steps, encoding it for agents locks in the problem at scale. And once workflows are distributed across agents, the challenge shifts from execution to governance: who owns the process, who validates it, and how it evolves when business conditions change.
It puts the onus on teams to take a hard look at what works for them and what doesn’t.
Organizations need to consider that, along with the execution control plane offered by platforms like Agentforce Operations, someone should be made responsible for task completion and success.
Brandon Metcalf, founder and CEO of workforce orchestration company Asymbl, told VentureBeat in a separate interview that the key to both humans and agents following a workflow is a shared goal.
“You have to understand the goal or the agent or human won’t complete the task successfully,” Metcalf said. “Someone has to manage that outcome that has to be delivered. It can be a person or an agent.”
The bottleneck has moved. As Metcalf framed it, the question is no longer whether agents can reason through a task, it's whether the workflow underneath them is coherent enough to execute. For enterprises that built their processes around human judgment and institutional memory, that's a harder fix than swapping in a smarter model.
