Agent Engineering Services | Enterprise AI Agent Development
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    AI agent development services that run in production

    We design and build agents you can trust with real work: the architecture, the tools, the orchestration and the guardrails.

    Talk to an agent engineer
    AI agent development: a central AI core connected to planning, coding, quality check, tools and data analysis agents

    What is changing in enterprise AI

    Agents are easy to demo. Getting them allowed to act is the hard part.

    40%+

    of agentic AI projects will be cancelled by the end of 2027 over cost, unclear value or weak risk controls

    Gartner ↗
    40%

    of large organisations now scale AI agents, up from 27% a year earlier

    McKinsey ↗
    33%

    of enterprise software apps will include agentic AI by 2028, up from under 1% in 2024

    Gartner ↗

    Sources: 1. Gartner, press release, 25 June 2025. 2. McKinsey, The state of AI in 2026, August 2026. 3. Gartner, press release, 25 June 2025.

    What we build

    Agentic AI development services, end to end

    Start from the decision, not the model

    Each agent is sized to what it may read, what it may change, and how much autonomy the risk allows.

    AI drafts the spec and risk tier · your process owner signs off

    Agent Studio →

    Agent design and architecture

    One agent per job. A team of agents per process.

    In multi-agent system development, a supervisor routes work to specialist agents and a verifier checks the result. One agent when one job will do.

    AI maps your process into a workflow · our architect signs off

    Agent Studio →

    Single and multi-agent systems

    Agents that act on your systems, even the old ones

    Every tool is registered with an owner and a scope. Legacy adapters reach systems with no modern API.

    AI generates connector configs · an engineer reviews every file

    Agent Studio →

    Tool, MCP and system integration

    Agents that answer from your content, and cite it

    Grounded in your authoritative sources, with each source system's access rules kept intact.

    AI builds the golden question set · your experts review the answers

    GenAI and RAG Solutions →

    Knowledge, memory and RAG

    Nothing ships without an evidence pack

    Every change passes its evaluation pack, and every release has a rollback that has already been tested.

    AI generates the test suites · your release manager signs off

    AgentOps →

    Evaluation and release

    When the prompt stops helping, we train the model

    Traces and reviewer overrides show the weakness. Fix the prompt and retrieval first, then the model.

    AI reads traces and proposes the fix · our AI lead signs off

    Data Science →

    Agent tuning and optimisation

    Agent Studio

    One planning cycle, four agents, one trace

    Agent Studio is where we build and run your agents. Here, four library agents share one planning cycle.

    • Start from library agents, adapted to your data
    • Routine steps run alone. Critical ones wait for approval.
    • Every decision logged, every action auditable
    Explore Agent Studio Book a demo
    Agent Studio: planning cycle with demand forecasting, inventory planning, pricing actions and sales optimisation agents

    The path

    Enterprise AI agent development, step by step

    1. FramePick one workflow
    2. DesignAgents, tools, approvals
    3. BuildInside your cloud
    4. EvaluateGolden set, red-team
    5. RunCanary and rollback
    Book a diagnostic →

    Guardrails that keep AI agents safe to run

    Nothing ships ungated

    Every prompt, model or tool change passes its evaluation suite before release.

    Nothing ships ungated: Permissions, Approval, Logs and Rollback gates

    • Permissions per tool

      Each tool carries its own scope. Nothing unregistered gets called.

    • Approval where it matters

      Routine steps run alone. Big ones wait for a named person.

    • Every action logged

      Audit can trace any decision back to its run.

    • Rollback, already tested

      Rehearsed in non-production before anything goes live.

    Inside 1Platform

    Results sooner, because your agents never start from zero

    Foundation Studio

    60–80%

    less data extraction time, so your first agent reads data that is already landed and checked.

    • Source connectors100+ sources, reused
    • Pipeline generationDrafted from your BRD or mapping
    • Data quality scorecardsChecked before agents read
    • Feature storeReady for ML Studio
    • Your cloudFabric, Databricks, Snowflake, GCP, AWS
    Explore Foundation Studio

    ML Studio

    40–60%

    less build effort, with pre-trained industry models your agents call as tools.

    • Industry model libraryForecasting, pricing, churn, risk
    • Models as agent toolsCalled under the same controls
    • Drift and bias monitoringAlerts before decisions slip
    • Retraining triggersKeeps forecasts current
    • Value validationBaseline vs uplift, A/B tested
    Explore ML Studio

    Governance Studio

    Every action

    traced from source data, through the model, to what the agent did.

    • End-to-end lineageSource to model to agent action
    • Policy enforcementSet once, applied across Studios
    • Access reviewRecertified, not assumed
    • AI observabilityDrift, bias and performance alerts
    • Agentic FinOpsLLM and cloud cost per use case
    Explore Governance Studio

    Where AI agent engineering connects

    Row of humanoid robot heads representing GenAI and RAG solutions

    GenAI and RAG Solutions

    Cited answers over your documents, often the knowledge tool an agent calls.

    Explore GenAI and RAG solutions →
    Humanoid AI over a city skyline representing agentic AI delivery

    Agentic AI Delivery

    Agentic AI across RGM, CPG, manufacturing and pharma.

    Explore agentic AI delivery →
    Neural network visual representing data science

    Data Science

    Forecasting and scoring models that agents call as tools.

    Explore data science →
    Talk to an agent engineer

    Latest agent engineering insights

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    What the Anaplan CoModeler Actually Does Inside an Implementation

    FAQ

    Questions teams ask before building AI agents

    No. Our AI agent implementation services also work on the framework you already run.

    It needs permission for that specific tool, and big actions wait for a named person to approve. Every step is logged.

    When one pass can’t do the job, or an independent check matters.

    Yes. Through APIs and MCP, in your own cloud, on models you’ve approved.

    Permissions, evaluation, release pipelines, and someone on call when an agent misbehaves.

    See what your first agentcould run

    Bring one workflow. We'll map the agents, tools and guardrails it needs.

    Talk to an agent engineer

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    Talk to our Experts

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    Bablu Chakraborty

    Senior Vice President

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    Ankur Sharma

    Ankur Sharma

    Assistant Vice President

    LinkedIn