Service
Production agentic systems for enterprise — designed, governed, and shipped to handle real work against real data.
Frequently Asked
Agentic AI refers to systems that combine large language models with tool use, memory, and planning to complete multi-step tasks autonomously. Unlike a single-turn chatbot, an agent decides which actions to take, executes them, observes the result, and iterates toward a goal — under defined constraints and human-approval boundaries.
Generative AI produces output. Agentic AI takes action. A generative system summarises a contract; an agentic system reads the contract, flags renewal risk, drafts a counter-clause, and routes it to legal for sign-off.
When the workflow is fully deterministic and high-volume — there a rules engine wins on cost, latency, and auditability. Agents earn their keep where decisions are ambiguous, the long tail dominates, and humans were already in the loop.
A scoped first agent runs 8–12 weeks from discovery to production. Multi-agent systems with sensitive data integrations typically take 16–24 weeks, with phased go-lives behind feature flags.
Token, infrastructure, and human-review costs vary by workload. We model unit economics during the pilot and tune routing, caching, and model-tier selection to bring per-task cost into a defensible range before scale.
From Demo to Production
What we build
We design and ship production agentic systems for enterprise teams — orchestration layers, tool integrations, retrieval and grounding, evals, and the governance scaffolding that keeps an agent operating inside policy. The work spans single-purpose agents that resolve one workflow end-to-end and multi-agent systems that coordinate across functions.
Our agents run against real data, with real compliance constraints, in environments where a wrong answer has consequences. That shapes every architectural choice we make.
How we approach it
We don’t start from a framework. We start from the failure surface.
- Decision mapping. What is the agent actually deciding? What inputs does it need, what tools must it call, and what does success look like — measurable, not aspirational?
- Failure inventory. Where will it break? Hallucinated tool calls, stale retrieval, prompt-injection from third-party content, policy violations, runaway cost. Each gets a control point.
- Eval-first build. We write the evaluation harness before the agent. Golden tasks, adversarial probes, drift monitors, regression suites — all wired into CI before a single production prompt fires.
- Phased exposure. Behind a feature flag, shadow mode first, then human-in-the-loop, then supervised autonomy. We rarely run an agent fully unattended; we make the supervision cheap.
- Production handover. SLOs, runbooks, on-call patterns, cost dashboards. The agent ships with the operational scaffolding to be owned by a real team.
Capabilities
- Agent orchestration on LangGraph, custom state machines, and event-driven runtimes.
- Tool integration against ERP, CRM, data warehouses, document stores, and legacy APIs.
- Retrieval and grounding over enterprise corpora — hybrid sparse/dense, reranking, citation enforcement.
- Eval harnesses: retrieval, grounding, answer quality, safety, and drift.
- Cost engineering: routing, caching, model-tier selection, structured output enforcement.
- Human-in-the-loop UX — review queues, override surfaces, audit trails.
- Policy interception — prompt and response guardrails, DLP, regulatory redaction.
Where it fits
We deploy agents where the value is concentrated and the controls are non-negotiable: regulated finance, energy operations, healthcare administration, public-sector citizen services, and engineering operations inside large platforms. Sectors below.
Why Eldridge Morgan
We build the systems. We do not deliver a deck and disappear. Every engagement ships running code, a maintainable eval suite, and a team that can operate the system once we leave. India-native cost discipline, global engineering standards, and a refusal to ship anything we couldn’t operate ourselves.
Sectors Served
- Energy (Oil & Gas)
- Financial Services
- Private Equity
- Logistics & Transportation
- Industrials & Manufacturing
- Healthcare
- Consumer & Retail
- Technology & Telecom
- Public Sector
Related Reading
- Why 80% of Enterprise AI Agents Never Reach Production
- Cutting Loan-Underwriting Cycle Time 70% at an Indian NBFC