Agentic AI development for coordinated workflows
Our agentic AI development work focuses on coordinating agents for complex workflows. Error handling, shared state, cost limits and human oversight help turn a demonstration into a system you can evaluate for production.
Agentic AI describes systems where language models plan and execute sequences of actions toward a goal with some degree of autonomy, calling tools and reacting to results rather than following a fixed script. Multi-agent systems coordinate several such agents, each with a defined role, on tasks too complex for one agent to handle reliably.
Agentic systems fail in ways single calls do not
A single model call either works or does not. An agentic system can complete four steps correctly, fail the fifth, and leave your data in a state that neither the old nor the new process expects.
Add multiple agents and it compounds: agents that loop passing work between each other, costs that spiral because nothing bounded the number of steps, and failures nobody can diagnose because there is no trace of what each agent decided and why.
Bounded, traced and recoverable
Every agent gets explicit limits: which tools it may call, how many steps it may take, how much it may spend, and what it must escalate rather than decide.
Every step is traced. Every state-changing action is idempotent or has a compensating rollback. And any action that is expensive, irreversible or customer-facing sits behind a human gate as a matter of architecture rather than policy.
Agentic AI development: scope and deliverables
Multi-agent architectures are frequently over-applied. Splitting a task across five agents adds coordination overhead, latency and failure modes, and for most problems a single well-designed agent with good tools performs better.
The genuine cases are where sub-tasks need materially different capabilities, tools or permissions, a research agent that only reads, a drafting agent that only writes, a review agent that checks the other two and has no ability to modify anything.
Separation of permissions is often the real justification. An agent that cannot write cannot corrupt anything, and having the checking agent be structurally unable to modify what it checks is a stronger guarantee than instructing it not to.
- Orchestration patterns: single agent, supervisor, or peer collaboration
- Per-agent tool scoping and permission boundaries
- Step, depth and spend limits enforced at the framework level
- Full step-level tracing across every agent in a run
- Idempotent actions and compensating rollback for partial failures
- Human approval gates on irreversible or high-consequence actions
For a single agent completing a bounded task, see AI agent development services. This page focuses on coordinating multiple agents, their shared state and their handoffs.
Where agentic systems are warranted
Work that genuinely requires planning: where the correct next step depends on what the previous step discovered, and the path cannot be enumerated in advance.
If the process can be drawn as a flowchart, build the flowchart. Deterministic automation is cheaper to run, easier to audit and more reliable, and we recommend it whenever it suffices.
- Tasks where the next step depends on what the previous step found
- Research and analysis work following a method rather than a script
- Processes needing several distinct capabilities and permission scopes
- Operations where exception paths are too varied to enumerate as rules
- Workflows currently requiring a specialist purely to navigate systems
- High-volume work where the per-task cost of an agent still beats a person
Benefits of agentic AI development
Handles genuine variation
Work where the path cannot be predetermined, which is exactly where rules engines accumulate exceptions and break.
Permissions enforced structurally
A review agent that cannot write is a stronger guarantee than a review agent instructed not to.
Costs that cannot run away
Step, depth and spend limits enforced by the framework, so a looping agent stops rather than billing all night.
Failures you can diagnose
Full step-level traces across every agent, so a bad outcome can be reconstructed rather than guessed at.
Recoverable partial failures
Idempotent actions and compensating rollback so a failure at step five does not leave inconsistent data.
Human oversight by design
Approval gates on consequential actions built into the architecture rather than added after an incident.
Business challenges agentic AI solves
Agents looping indefinitely
Work passed back and forth without converging. Depth and step limits with escalation stop it.
Runaway costs overnight
An unbounded run consuming a month of budget. Spend caps enforced per run and per agent.
Partial failures corrupting state
Four steps applied, fifth failed. Idempotency and compensating rollback make runs recoverable.
No way to explain an outcome
Opaque multi-agent decisions. Step-level tracing makes every run reconstructable.
Over-engineered agent swarms
Five agents where one would do better. We recommend the simplest architecture that works.
Autonomy without oversight
Systems taking consequential actions unchecked. Approval gates on anything irreversible.
Features and deliverables
Everything below is in scope on a standard engagement. Nothing here is an upsell discovered halfway through the build.
Architecture design
Orchestration pattern, agent roles, tool allocation and permission boundaries designed and signed off before build.
Tool and permission scoping
Per-agent tool access with least-privilege defaults, so capability is granted deliberately rather than broadly.
Planning and decomposition
Task decomposition with explicit bounds on depth, branching and retry, preventing unbounded exploration.
State management
Durable run state so a long-running task survives restarts, with resumption from the last completed step.
Guardrails and limits
Spend caps, step limits, output validation, prompt-injection defences and escalation triggers on every agent.
Observability and tracing
Full traces of every agent decision, tool call and result, with cost and latency attributed per step.
Human-in-the-loop workflow
An approval queue surfacing gated actions with the agent’s plan and reasoning for a fast decision.
Evaluation harness
Regression suites over real scenarios measuring end-to-end task success, not just individual model outputs.
Technologies we use for agentic AI development
We are not tied to one vendor. Model and infrastructure choices are made on accuracy, cost per task, latency, and where your data is allowed to live.
Our AI development process
The same five stages on every engagement, so you always know what happens next and what you get at the end of it.
Discovery
We interview the people doing the work, map the workflow end to end, and audit the systems and data behind it.
AI Strategy
Every opportunity gets scored on cost to build, time to value, and annual savings, then ranked.
Pilot Build
We ship the top-ranked automation as a fixed-scope pilot so you see real output before committing further budget.
Implementation
Integration with your live systems, staff training, human-in-the-loop review gates, and a documented rollback path.
Optimization
Monthly accuracy reviews, prompt and retrieval tuning, and a written report on hours and dollars saved.
How long it takes
A typical first engagement, week by week. Complex integrations and regulated environments extend this, and we say so during discovery rather than after.
Architecture and scoping
Agent roles, tools, permissions, escalation rules and failure paths designed and agreed.
Single-agent prototype
The core capability proven with one agent before adding coordination complexity.
Full system build
Orchestration, tool integration, guardrails, state management and approval workflow.
Reliability engineering
Failure injection, rollback testing, cost bound verification and evaluation suite build.
Supervised launch
Live with every run reviewed initially, autonomy widened per category as evidence accumulates.
Industries we deliver agentic AI development for
SaaS & Technology
AI features inside your product, support deflection, onboarding assistants, and usage analytics.
Financial Services
Document extraction, reconciliation, KYC support, and audit-ready reporting with full traceability.
Logistics & Supply Chain
Document processing, carrier communication, exception handling, and inventory rebalancing.
Insurance
First-notice-of-loss intake, claims triage, policy Q&A, and fraud signal detection.
Professional Services
Proposal drafting, timesheet capture, research synthesis, and client reporting at scale.
Manufacturing
Quality inspection, maintenance prediction, supplier communication, and production scheduling.
Healthcare
Intake, prior authorization, clinical documentation, and revenue-cycle workflows built to respect HIPAA boundaries.
Retail & E-commerce
Product data enrichment, demand forecasting, support deflection, and personalized merchandising.
Real-world agentic AI use cases
Multi-system operations resolution
An agent gathering context across systems, diagnosing, and executing a fix within defined limits.
Research and analysis workflows
Agents following a repeatable research method across sources and synthesizing a cited output.
Complex claims assessment
Specialist agents handling documents, coverage and calculation, with an adjuster approving the recommendation.
Procurement and sourcing
Agents gathering quotes, checking contract compliance and preparing purchase recommendations for approval.
Code review and remediation
Agents analysing changes, proposing fixes and preparing pull requests for human approval.
Data quality remediation
Agents finding inconsistencies across systems, investigating causes, and proposing or applying bounded corrections.
Why choose DevSolutionsAI for agentic AI development
Business case before build
Every recommendation carries an estimated cost, timeline, and annual savings figure. If the math does not work, we say so before you spend.
Vendor-neutral by design
We resell nothing and take no platform commissions. Model and infrastructure choices are made on fit, cost, and your data-residency rules.
Fixed-scope pilots
The first engagement is a defined deliverable at a defined price, not an open-ended retainer that quietly grows each quarter.
Built for handover
You own the code, the prompts, the infrastructure, and the documentation. No lock-in to a proprietary wrapper you cannot leave.
Human-in-the-loop where it counts
Anything customer-facing, clinical, financial, or legal gets a review gate, a confidence threshold, and a logged audit trail.
Security reviewed early
Data flow diagrams, retention rules, and access boundaries are agreed in week one, not retrofitted after your security team objects.
Find out what agentic AI development would cost you, before you commit to anything
Every engagement is quoted after a short discovery, so you get a fixed written price built around your actual volumes rather than a rate card that assumes someone else’s business.
The first call is thirty minutes and free. Bring one workflow. We will tell you what it is likely costing you each year, roughly what automating it would take, and whether we think it is worth doing at all.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
Illustrative project scenario
Agents handling exceptions, with humans keeping the money decisions
Challenge. A freight brokerage handled roughly 3,000 shipment exceptions monthly, delays, damages, address failures, capacity shortfalls. Each required gathering information across four systems, contacting parties, and deciding a resolution. Paths varied too much for rules; a previous rules engine had accumulated 400 conditions and still missed cases.
What we built. A supervisor agent triaging exceptions to three specialists: an investigation agent with read-only access across systems, a communication agent that drafts but cannot send without approval, and a resolution agent that can apply fixes below a defined value threshold. Anything involving customer credit, carrier penalties or amounts above the threshold routes to a human with the full investigation attached.
Outcome. Around 71% of exceptions now resolve without human involvement, all within the value threshold. Median resolution time fell from 4.2 hours to 24 minutes. No financial decision above the threshold has ever been taken autonomously, which was the condition of deployment.
Illustrative project scenario. The figures demonstrate how a project could be scoped and evaluated; they are not verified client results or an audited average.
What clients say about working with us
Agentic AI Development FAQs
Do we need multiple agents or would one do?
Usually one, and this is the most common over-engineering we see. Splitting work across five agents adds coordination overhead, latency and failure modes. Multiple agents genuinely help when sub-tasks need materially different tools or permissions, particularly when you want structural separation, such as a review agent that is physically unable to modify what it reviews. Otherwise a single agent with good tools performs better.
How do you stop agents from looping or running up costs?
Hard limits enforced by the framework rather than requested in a prompt: maximum steps per run, maximum recursion depth, and a spend cap per run and per agent. When a limit is hit the run stops and escalates to a human rather than continuing. This is enforced in code, because an instruction to “be efficient” is not a control.
What happens if an agent fails partway through a task?
It is designed for explicitly. State-changing actions are made idempotent where possible so a retry does not double-apply, and sequences that cannot be made idempotent get a compensating rollback path. Run state is durable, so a task can resume from the last completed step rather than restarting. Failures are traced, alert an owner, and surface for review.
How much autonomy should we give an agent?
Start with almost none and widen it with evidence. Our standard approach is that every run is human-reviewed at launch, and autonomy is enabled per action category once measured success on that category justifies it. Irreversible, financial and customer-facing actions typically keep a permanent approval gate regardless of how well the agent performs, because the downside is asymmetric.
Which agent framework do you use?
It depends on the requirement. We work with LangGraph, the Model Context Protocol, and purpose-built orchestration where a framework adds more complexity than it removes. For many production systems the right answer is relatively plain code with explicit state management rather than a framework, because frameworks optimize for expressiveness and production optimizes for predictability.
How do you test an agentic system?
End-to-end task success on real scenarios, not just individual model outputs. We build a regression suite of actual cases with known correct outcomes and score whether the agent completed the task properly, including whether it escalated when it should have. We also run failure injection, making tools fail mid-run, to verify recovery paths work rather than assuming they do.
Services that pair well with this one
Most clients combine two or three of these. We will tell you the right sequence during discovery.
Ready to scope your agentic AI development project?
Book a free 30-minute consultation. Bring one workflow and leave with a realistic estimate of what it would cost to automate and what it would save.